Inter prediction with refinement in video processing

ABSTRACT

Devices, systems and methods for digital video coding, which includes inter prediction with refinement, are described. An exemplary method of video processing includes determining to use, for a conversion between a current block of a video and a bitstream representation of the video, a first linear optimization model for the conversion using a first coding mode, the first linear optimization model being derived from a second linear optimization model that is used for the conversion using a second coding mode, and performing, based on the determining, the conversion. Another exemplary method of video processing includes determining to use, for a conversion between a current block of a video and a bitstream representation of the video, a gradient value computation algorithm for a bi-directional optical flow tool, and performing, based on the determining, the conversion.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/998,406, filed on Aug. 20, 2020, which is a continuation ofInternational Application No. PCT/CN2019/115695, filed on Nov. 5, 2019,which claims the priority to and benefits of International PatentApplication No. PCT/CN2018/113928, filed on Nov. 5, 2018. All theaforementioned patent applications are hereby incorporated by referencein their entireties.

TECHNICAL FIELD

This patent document relates to video coding techniques, devices andsystems.

BACKGROUND

In spite of the advances in video compression, digital video stillaccounts for the largest bandwidth use on the internet and other digitalcommunication networks. As the number of connected user devices capableof receiving and displaying video increases, it is expected that thebandwidth demand for digital video usage will continue to grow.

SUMMARY

Devices, systems and methods related to digital video coding, andspecifically, to harmonization of linear mode prediction for videocoding. The described methods may be applied to both the existing videocoding standards (e.g., High Efficiency Video Coding (HEVC)) and futurevideo coding standards or video codecs.

In one representative aspect, the disclosed technology may be used toprovide a method of video processing. This method includes determiningto use, for a conversion between a current block of a video and abitstream representation of the video, a first linear optimization modelfor the conversion using a first coding mode, the first linearoptimization model being derived from a second linear optimization modelthat is used for the conversion using a second coding mode; andperforming, based on the determining, the conversion.

In another representative aspect, the disclosed technology may be usedto provide a method of video processing. This method includes enabling,based on one or more picture order count (POC) parameters associatedwith a picture of a current block of video, either a first predictionmode or a second prediction mode different from the first predictionmode, the first prediction mode being a coding mode using optical flow;and performing, based on the first mode or the second mode, a conversionbetween the current block and a bitstream representation of the video.

In yet another representative aspect, the disclosed technology may beused to provide a method of video processing. This method includesconsecutively deriving, based on coded information associated with acurrent block of video, one or more velocity vectors (v_(x), v_(y))associated with a reference picture of the current block; andperforming, based on the one or more velocity vectors, a conversionbetween the current block and a bitstream representation of the video,the coded information comprising a value of a horizontal component of amotion vector of the current block, a value of a vertical component ofthe motion vector of the current block, or a size of the current block.

In yet another representative aspect, the disclosed technology may beused to provide a method of video processing. This method includesperforming, upon a determination that a coding mode using optical flowhas been enabled for a current block of video, a filtering operationusing a single type of interpolation filter for each color component ofthe current block; and performing, based on the filtering operation, aconversion between the current block and a bitstream representation ofthe video.

In yet another representative aspect, the disclosed technology may beused to provide a method of video processing. This method includesperforming, upon a determination that a coding mode using optical flowhas been enabled for a current block of video, a filtering operationusing a single type of interpolation filter for each color component ofthe current block; performing, upon a determination that at least onesample of the current block is located outside a predetermined range, apadding operation; and performing, based on the filtering operation andthe padding operation, a conversion between the current block and abitstream representation of the video.

In yet another representative aspect, the disclosed technology may beused to provide a method of video processing. This method includesdetermining to use, for a conversion between a current block of a videoand a bitstream representation of the video, a gradient valuecomputation algorithm for an optical flow tool; and performing, based onthe determining, the conversion.

In yet another representative aspect, the disclosed technology may beused to provide a method of video processing. This method includesmaking a decision, based on one or more sum of absolute difference (SAD)calculations for a sub-block of a current block of video, regarding aselective enablement of a coding mode using optical flow for the currentblock; and performing, based on the decision, a conversion between thecurrent block and a bitstream representation of the current block.

In yet another representative aspect, the disclosed technology may beused to provide a method of video processing. This method includesderiving, based on a selective enablement of a generalized bi-predictionimprovement (GBi) process for a current block of video, one or moreparameters of a coding mode using optical flow for the current block;and performing, based on the one or more parameters of the coding modeusing optical flow, a conversion between the current block and abitstream representation of the video.

In yet another representative aspect, the disclosed technology may beused to provide a method of video processing. This method includesperforming, for a current block of video coded with a coding mode usingoptical flow, a clipping operation on a final prediction output of thecoding mode using optical flow; and performing, based on the finalprediction output, a conversion between the current block and abitstream representation of the video.

In yet another representative aspect, the above-described method isembodied in the form of processor-executable code and stored in acomputer-readable program medium.

In yet another representative aspect, a device that is configured oroperable to perform the above-described method is disclosed. The devicemay include a processor that is programmed to implement this method.

In yet another representative aspect, a video decoder apparatus mayimplement a method as described herein.

The above and other aspects and features of the disclosed technology aredescribed in greater detail in the drawings, the description and theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of constructing a merge candidate list.

FIG. 2 shows an example of positions of spatial candidates.

FIG. 3 shows an example of candidate pairs subject to a redundancy checkof spatial merge candidates.

FIGS. 4A and 4B show examples of the position of a second predictionunit (PU) based on the size and shape of the current block.

FIG. 5 shows an example of motion vector scaling for temporal mergecandidates.

FIG. 6 shows an example of candidate positions for temporal mergecandidates.

FIG. 7 shows an example of generating a combined bi-predictive mergecandidate.

FIG. 8 shows an example of constructing motion vector predictioncandidates.

FIG. 9 shows an example of motion vector scaling for spatial motionvector candidates.

FIG. 10 shows an example of motion prediction using the alternativetemporal motion vector prediction (ATMVP) algorithm for a coding unit(CU).

FIG. 11 shows an example of a coding unit (CU) with sub-blocks andneighboring blocks used by the spatial-temporal motion vector prediction(STMVP) algorithm.

FIGS. 12A and 12B show example snapshots of sub-block when using theoverlapped block motion compensation (OBMC) algorithm.

FIG. 13 shows an example of neighboring samples used to deriveparameters for the local illumination compensation (LIC) algorithm.

FIG. 14 shows an example of a simplified affine motion model.

FIG. 15 shows an example of an affine motion vector field (MVF) persub-block.

FIG. 16 shows an example of motion vector prediction (MVP) for theAF_INTER affine motion mode.

FIGS. 17A and 17B show example candidates for the AF_MERGE affine motionmode.

FIG. 18 shows an example of bilateral matching in pattern matched motionvector derivation (PMMVD) mode, which is a special merge mode based onthe frame-rate up conversion (FRUC) algorithm.

FIG. 19 shows an example of template matching in the FRUC algorithm.

FIG. 20 shows an example of unilateral motion estimation in the FRUCalgorithm.

FIG. 21 shows an example of an optical flow trajectory used by thebi-directional optical flow (BDOF) algorithm.

FIGS. 22A and 22B show example snapshots of using of the bi-directionaloptical flow (BDOF) algorithm without block extensions.

FIG. 23 shows an example of the interpolated samples used in BDOF.

FIG. 24 shows an example of the decoder-side motion vector refinement(DMVR) algorithm based on bilateral template matching.

FIG. 25 shows an example of the locations of samples used for thederivation of parameters of the linear model (α and β) in a linearprediction mode.

FIG. 26 shows an example of a straight line (representative of a linearmodel) between the maximum and the minimum luma values.

FIG. 27 shows another example of interpolated samples used in BDOF.

FIGS. 28A-28I show flowcharts of example methods for video processing.

FIG. 29 is a block diagram of an example of a hardware platform forimplementing a visual media decoding or a visual media encodingtechnique described in the present document.

FIG. 30 is a block diagram of an example video processing system inwhich disclosed techniques may be implemented.

DETAILED DESCRIPTION

Due to the increasing demand of higher resolution video, video codingmethods and techniques are ubiquitous in modern technology. Video codecstypically include an electronic circuit or software that compresses ordecompresses digital video, and are continually being improved toprovide higher coding efficiency. A video codec converts uncompressedvideo to a compressed format or vice versa. There are complexrelationships between the video quality, the amount of data used torepresent the video (determined by the bit rate), the complexity of theencoding and decoding algorithms, sensitivity to data losses and errors,ease of editing, random access, and end-to-end delay (latency). Thecompressed format usually conforms to a standard video compressionspecification, e.g., the High Efficiency Video Coding (HEVC) standard(also known as H.265 or MPEG-H Part 2), the Versatile Video Codingstandard to be finalized, or other current and/or future video codingstandards.

Embodiments of the disclosed technology may be applied to existing videocoding standards (e.g., HEVC, H.265) and future standards to improvecompression performance. Section headings are used in the presentdocument to improve readability of the description and do not in any waylimit the discussion or the embodiments (and/or implementations) to therespective sections only.

1. Examples of Inter-Prediction in HEVC/H.265

Video coding standards have significantly improved over the years, andnow provide, in part, high coding efficiency and support for higherresolutions. Recent standards such as HEVC and H.265 are based on thehybrid video coding structure wherein temporal prediction plus transformcoding are utilized.

1.1 Examples of Prediction Modes

Each inter-predicted PU (prediction unit) has motion parameters for oneor two reference picture lists. In some embodiments, motion parametersinclude a motion vector and a reference picture index. In otherembodiments, the usage of one of the two reference picture lists mayalso be signaled using inter_pred_idc. In yet other embodiments, motionvectors may be explicitly coded as deltas relative to predictors.

When a CU is coded with skip mode, one PU is associated with the CU, andthere are no significant residual coefficients, no coded motion vectordelta or reference picture index. A merge mode is specified whereby themotion parameters for the current PU are obtained from neighboring PUs,including spatial and temporal candidates. The merge mode can be appliedto any inter-predicted PU, not only for skip mode. The alternative tomerge mode is the explicit transmission of motion parameters, wheremotion vector, corresponding reference picture index for each referencepicture list and reference picture list usage are signaled explicitlyper each PU.

When signaling indicates that one of the two reference picture lists isto be used, the PU is produced from one block of samples. This isreferred to as ‘uni-prediction’. Uni-prediction is available both forP-slices and B-slices.

When signaling indicates that both of the reference picture lists are tobe used, the PU is produced from two blocks of samples. This is referredto as ‘bi-prediction’. Bi-prediction is available for B-slices only.

1.1.1 Embodiments of Constructing Candidates for Merge Mode

When a PU is predicted using merge mode, an index pointing to an entryin the merge candidates list is parsed from the bitstream and used toretrieve the motion information. The construction of this list can besummarized according to the following sequence of steps:

Step 1: Initial candidates derivation

-   -   Step 1.1: Spatial candidates derivation    -   Step 1.2: Redundancy check for spatial candidates    -   Step 1.3: Temporal candidates derivation

Step 2: Additional candidates insertion

-   -   Step 2.1: Creation of bi-predictive candidates    -   Step 2.2: Insertion of zero motion candidates

FIG. 1 shows an example of constructing a merge candidate list based onthe sequence of steps summarized above. For spatial merge candidatederivation, a maximum of four merge candidates are selected amongcandidates that are located in five different positions. For temporalmerge candidate derivation, a maximum of one merge candidate is selectedamong two candidates. Since constant number of candidates for each PU isassumed at decoder, additional candidates are generated when the numberof candidates does not reach to maximum number of merge candidate(MaxNumMergeCand) which is signalled in slice header. Since the numberof candidates is constant, index of best merge candidate is encodedusing truncated unary binarization (TU). If the size of CU is equal to8, all the PUs of the current CU share a single merge candidate list,which is identical to the merge candidate list of the 2N×2N predictionunit.

1.1.2 Constructing Spatial Merge Candidates

In the derivation of spatial merge candidates, a maximum of four mergecandidates are selected among candidates located in the positionsdepicted in FIG. 2. The order of derivation is A₁, B₁, B₀, A₀ and B₂.Position B₂ is considered only when any PU of position A₁, B₁, B₀, A₀ isnot available (e.g. because it belongs to another slice or tile) or isintra coded. After candidate at position A₁ is added, the addition ofthe remaining candidates is subject to a redundancy check which ensuresthat candidates with same motion information are excluded from the listso that coding efficiency is improved.

To reduce computational complexity, not all possible candidate pairs areconsidered in the mentioned redundancy check. Instead only the pairslinked with an arrow in FIG. 3 are considered and a candidate is onlyadded to the list if the corresponding candidate used for redundancycheck has not the same motion information. Another source of duplicatemotion information is the “second PU” associated with partitionsdifferent from 2N×2N. As an example, FIGS. 4A and 4B depict the secondPU for the case of N×2N and 2N×N, respectively. When the current PU ispartitioned as N×2N, candidate at position A₁ is not considered for listconstruction. In some embodiments, adding this candidate may lead to twoprediction units having the same motion information, which is redundantto just have one PU in a coding unit. Similarly, position B₁ is notconsidered when the current PU is partitioned as 2N×N.

1.1.3 Constructing Temporal Merge Candidates

In this step, only one candidate is added to the list. Particularly, inthe derivation of this temporal merge candidate, a scaled motion vectoris derived based on co-located PU belonging to the picture which has thesmallest POC difference with current picture within the given referencepicture list. The reference picture list to be used for derivation ofthe co-located PU is explicitly signaled in the slice header.

FIG. 5 shows an example of the derivation of the scaled motion vectorfor a temporal merge candidate (as the dotted line), which is scaledfrom the motion vector of the co-located PU using the POC distances, tband td, where tb is defined to be the POC difference between thereference picture of the current picture and the current picture and tdis defined to be the POC difference between the reference picture of theco-located picture and the co-located picture. The reference pictureindex of temporal merge candidate is set equal to zero. For a B-slice,two motion vectors, one is for reference picture list 0 and the other isfor reference picture list 1, are obtained and combined to make thebi-predictive merge candidate.

In the co-located PU (Y) belonging to the reference frame, the positionfor the temporal candidate is selected between candidates C₀ and C₁, asdepicted in FIG. 6. If PU at position C₀ is not available, is intracoded, or is outside of the current CTU, position C₁ is used. Otherwise,position C₀ is used in the derivation of the temporal merge candidate.

1.1.4 Constructing Additional Types of Merge Candidates

Besides spatio-temporal merge candidates, there are two additional typesof merge candidates: combined bi-predictive merge candidate and zeromerge candidate. Combined bi-predictive merge candidates are generatedby utilizing spatio-temporal merge candidates. Combined bi-predictivemerge candidate is used for B-Slice only. The combined bi-predictivecandidates are generated by combining the first reference picture listmotion parameters of an initial candidate with the second referencepicture list motion parameters of another. If these two tuples providedifferent motion hypotheses, they will form a new bi-predictivecandidate.

FIG. 7 shows an example of this process, wherein two candidates in theoriginal list (710, on the left), which have mvL0 and refIdxL0 or mvL1and refIdxL1, are used to create a combined bi-predictive mergecandidate added to the final list (720, on the right).

Zero motion candidates are inserted to fill the remaining entries in themerge candidates list and therefore hit the MaxNumMergeCand capacity.These candidates have zero spatial displacement and a reference pictureindex which starts from zero and increases every time a new zero motioncandidate is added to the list. The number of reference frames used bythese candidates is one and two for uni- and bi-directional prediction,respectively. In some embodiments, no redundancy check is performed onthese candidates.

1.1.5 Examples of Motion Estimation Regions for Parallel Processing

To speed up the encoding process, motion estimation can be performed inparallel whereby the motion vectors for all prediction units inside agiven region are derived simultaneously. The derivation of mergecandidates from spatial neighborhood may interfere with parallelprocessing as one prediction unit cannot derive the motion parametersfrom an adjacent PU until its associated motion estimation is completed.To mitigate the trade-off between coding efficiency and processinglatency, a motion estimation region (MER) may be defined. The size ofthe MER may be signaled in the picture parameter set (PPS) using the“log 2_parallel_merge_level_minus2” syntax element. When a MER isdefined, merge candidates falling in the same region are marked asunavailable and therefore not considered in the list construction.

1.2 Embodiments of Advanced Motion Vector Prediction (AMVP)

AMVP exploits spatio-temporal correlation of motion vector withneighboring PUs, which is used for explicit transmission of motionparameters. It constructs a motion vector candidate list by firstlychecking availability of left, above temporally neighboring PUpositions, removing redundant candidates and adding zero vector to makethe candidate list to be constant length. Then, the encoder can selectthe best predictor from the candidate list and transmit thecorresponding index indicating the chosen candidate. Similarly withmerge index signaling, the index of the best motion vector candidate isencoded using truncated unary. The maximum value to be encoded in thiscase is 2 (see FIG. 8). In the following sections, details aboutderivation process of motion vector prediction candidate are provided.

1.2.1 Examples of Constructing Motion Vector Prediction Candidates

FIG. 8 summarizes derivation process for motion vector predictioncandidate, and may be implemented for each reference picture list withrefidx as an input.

In motion vector prediction, two types of motion vector candidates areconsidered: spatial motion vector candidate and temporal motion vectorcandidate. For spatial motion vector candidate derivation, two motionvector candidates are eventually derived based on motion vectors of eachPU located in five different positions as previously shown in FIG. 2.

For temporal motion vector candidate derivation, one motion vectorcandidate is selected from two candidates, which are derived based ontwo different co-located positions. After the first list ofspatio-temporal candidates is made, duplicated motion vector candidatesin the list are removed. If the number of potential candidates is largerthan two, motion vector candidates whose reference picture index withinthe associated reference picture list is larger than 1 are removed fromthe list. If the number of spatio-temporal motion vector candidates issmaller than two, additional zero motion vector candidates is added tothe list.

1.2.2 Constructing Spatial Motion Vector Candidates

In the derivation of spatial motion vector candidates, a maximum of twocandidates are considered among five potential candidates, which arederived from PUs located in positions as previously shown in FIG. 2,those positions being the same as those of motion merge. The order ofderivation for the left side of the current PU is defined as A₀, A₁, andscaled A₀, scaled A₁. The order of derivation for the above side of thecurrent PU is defined as B₀, B₁, B₂, scaled B₀, scaled B₁, scaled B₂.For each side there are therefore four cases that can be used as motionvector candidate, with two cases not required to use spatial scaling,and two cases where spatial scaling is used. The four different casesare summarized as follows:

-   -   No spatial scaling        -   (1) Same reference picture list, and same reference picture            index (same POC)        -   (2) Different reference picture list, but same reference            picture (same POC)    -   Spatial scaling        -   (3) Same reference picture list, but different reference            picture (different POC)        -   (4) Different reference picture list, and different            reference picture (different POC)

The no-spatial-scaling cases are checked first followed by the casesthat allow spatial scaling. Spatial scaling is considered when the POCis different between the reference picture of the neighbouring PU andthat of the current PU regardless of reference picture list. If all PUsof left candidates are not available or are intra coded, scaling for theabove motion vector is allowed to help parallel derivation of left andabove MV candidates. Otherwise, spatial scaling is not allowed for theabove motion vector.

As shown in the example in FIG. 9, for the spatial scaling case, themotion vector of the neighbouring PU is scaled in a similar manner asfor temporal scaling. One difference is that the reference picture listand index of current PU is given as input; the actual scaling process isthe same as that of temporal scaling.

1.2.3 Constructing Temporal Motion Vector Candidates

Apart from the reference picture index derivation, all processes for thederivation of temporal merge candidates are the same as for thederivation of spatial motion vector candidates (as shown in the examplein FIG. 6). In some embodiments, the reference picture index is signaledto the decoder.

2. Example of Inter Prediction Methods in Joint Exploration Model (JEM)

In some embodiments, future video coding technologies are explored usinga reference software known as the Joint Exploration Model (JEM). In JEM,sub-block based prediction is adopted in several coding tools, such asaffine prediction, alternative temporal motion vector prediction(ATMVP), spatial-temporal motion vector prediction (STMVP),bi-directional optical flow (BDOF or BIO), Frame-Rate Up Conversion(FRUC), Locally Adaptive Motion Vector Resolution (LAMVR), OverlappedBlock Motion Compensation (OBMC), Local Illumination Compensation (LIC),and Decoder-side Motion Vector Refinement (DMVR).

2.1 Examples of Sub-CU Based Motion Vector Prediction

In the JEM with quadtrees plus binary trees (QTBT), each CU can have atmost one set of motion parameters for each prediction direction. In someembodiments, two sub-CU level motion vector prediction methods areconsidered in the encoder by splitting a large CU into sub-CUs andderiving motion information for all the sub-CUs of the large CU.Alternative temporal motion vector prediction (ATMVP) method allows eachCU to fetch multiple sets of motion information from multiple blockssmaller than the current CU in the collocated reference picture. Inspatial-temporal motion vector prediction (STMVP) method motion vectorsof the sub-CUs are derived recursively by using the temporal motionvector predictor and spatial neighbouring motion vector. In someembodiments, and to preserve more accurate motion field for sub-CUmotion prediction, the motion compression for the reference frames maybe disabled.

2.1.1 Examples of Alternative Temporal Motion Vector Prediction (ATMVP)

In the ATMVP method, the temporal motion vector prediction (TMVP) methodis modified by fetching multiple sets of motion information (includingmotion vectors and reference indices) from blocks smaller than thecurrent CU.

FIG. 10 shows an example of ATMVP motion prediction process for a CU1000. The ATMVP method predicts the motion vectors of the sub-CUs 1001within a CU 1000 in two steps. The first step is to identify thecorresponding block 1051 in a reference picture 1050 with a temporalvector. The reference picture 1050 is also referred to as the motionsource picture. The second step is to split the current CU 1000 intosub-CUs 1001 and obtain the motion vectors as well as the referenceindices of each sub-CU from the block corresponding to each sub-CU.

In the first step, a reference picture 1050 and the corresponding blockis determined by the motion information of the spatial neighboringblocks of the current CU 1000. To avoid the repetitive scanning processof neighboring blocks, the first merge candidate in the merge candidatelist of the current CU 1000 is used. The first available motion vectoras well as its associated reference index are set to be the temporalvector and the index to the motion source picture. This way, thecorresponding block may be more accurately identified, compared withTMVP, wherein the corresponding block (sometimes called collocatedblock) is always in a bottom-right or center position relative to thecurrent CU.

In the second step, a corresponding block of the sub-CU 1051 isidentified by the temporal vector in the motion source picture 1050, byadding to the coordinate of the current CU the temporal vector. For eachsub-CU, the motion information of its corresponding block (e.g., thesmallest motion grid that covers the center sample) is used to derivethe motion information for the sub-CU. After the motion information of acorresponding N×N block is identified, it is converted to the motionvectors and reference indices of the current sub-CU, in the same way asTMVP of HEVC, wherein motion scaling and other procedures apply. Forexample, the decoder checks whether the low-delay condition (e.g. thePOCs of all reference pictures of the current picture are smaller thanthe POC of the current picture) is fulfilled and possibly uses motionvector MVx (e.g., the motion vector corresponding to reference picturelist X) to predict motion vector MVy (e.g., with X being equal to 0 or 1and Y being equal to 1-X) for each sub-CU.

2.1.2 Examples of Spatial-Temporal Motion Vector Prediction (STMVP)

In the STMVP method, the motion vectors of the sub-CUs are derivedrecursively, following raster scan order. FIG. 11 shows an example ofone CU with four sub-blocks and neighboring blocks. Consider an 8×8 CU1100 that includes four 4×4 sub-CUs A (1101), B (1102), C (1103), and D(1104). The neighboring 4×4 blocks in the current frame are labelled asa (1111), b (1112), c (1113), and d (1114).

The motion derivation for sub-CU A starts by identifying its two spatialneighbors. The first neighbor is the N×N block above sub-CU A 1101(block c 1113). If this block c (1113) is not available or is intracoded the other N×N blocks above sub-CU A (1101) are checked (from leftto right, starting at block c 1113). The second neighbor is a block tothe left of the sub-CU A 1101 (block b 1112). If block b (1112) is notavailable or is intra coded other blocks to the left of sub-CU A 1101are checked (from top to bottom, staring at block b 1112). The motioninformation obtained from the neighboring blocks for each list is scaledto the first reference frame for a given list. Next, temporal motionvector predictor (TMVP) of sub-block A 1101 is derived by following thesame procedure of TMVP derivation as specified in HEVC. The motioninformation of the collocated block at block D 1104 is fetched andscaled accordingly. Finally, after retrieving and scaling the motioninformation, all available motion vectors are averaged separately foreach reference list. The averaged motion vector is assigned as themotion vector of the current sub-CU.

2.1.3 Examples of Sub-CU Motion Prediction Mode Signaling

In some embodiments, the sub-CU modes are enabled as additional mergecandidates and there is no additional syntax element required to signalthe modes. Two additional merge candidates are added to merge candidateslist of each CU to represent the ATMVP mode and STMVP mode. In otherembodiments, up to seven merge candidates may be used, if the sequenceparameter set indicates that ATMVP and STMVP are enabled. The encodinglogic of the additional merge candidates is the same as for the mergecandidates in the HM, which means, for each CU in P or B slice, two moreRD checks may be needed for the two additional merge candidates. In someembodiments, e.g., JEM, all bins of the merge index are context coded byCABAC (Context-based Adaptive Binary Arithmetic Coding). In otherembodiments, e.g., HEVC, only the first bin is context coded and theremaining bins are context by-pass coded.

2.2 Examples of Adaptive Motion Vector Difference Resolution

In some embodiments, motion vector differences (MVDs) (between themotion vector and predicted motion vector of a PU) are signalled inunits of quarter luma samples when use_integer_mv_flag is equal to 0 inthe slice header. In the JEM, a locally adaptive motion vectorresolution (LAMVR) is introduced. In the JEM, MVD can be coded in unitsof quarter luma samples, integer luma samples or four luma samples. TheMVD resolution is controlled at the coding unit (CU) level, and MVDresolution flags are conditionally signalled for each CU that has atleast one non-zero MVD components.

For a CU that has at least one non-zero MVD components, a first flag issignalled to indicate whether quarter luma sample MV precision is usedin the CU. When the first flag (equal to 1) indicates that quarter lumasample MV precision is not used, another flag is signalled to indicatewhether integer luma sample MV precision or four luma sample MVprecision is used.

When the first MVD resolution flag of a CU is zero, or not coded for aCU (meaning all MVDs in the CU are zero), the quarter luma sample MVresolution is used for the CU. When a CU uses integer-luma sample MVprecision or four-luma-sample MV precision, the MVPs in the AMVPcandidate list for the CU are rounded to the corresponding precision.

In the encoder, CU-level RD checks are used to determine which MVDresolution is to be used for a CU. That is, the CU-level RD check isperformed three times for each MVD resolution. To accelerate encoderspeed, the following encoding schemes are applied in the JEM:

-   -   During RD check of a CU with normal quarter luma sample MVD        resolution, the motion information of the current CU (integer        luma sample accuracy) is stored. The stored motion information        (after rounding) is used as the starting point for further small        range motion vector refinement during the RD check for the same        CU with integer luma sample and 4 luma sample MVD resolution so        that the time-consuming motion estimation process is not        duplicated three times.    -   RD check of a CU with 4 luma sample MVD resolution is        conditionally invoked. For a CU, when RD cost integer luma        sample MVD resolution is much larger than that of quarter luma        sample MVD resolution, the RD check of 4 luma sample MVD        resolution for the CU is skipped.

2.3 Examples of Higher Motion Vector Storage Accuracy

In HEVC, motion vector accuracy is one-quarter pel (one-quarter lumasample and one-eighth chroma sample for 4:2:0 video). In the JEM, theaccuracy for the internal motion vector storage and the merge candidateincreases to 1/16 pel. The higher motion vector accuracy ( 1/16 pel) isused in motion compensation inter prediction for the CU coded withskip/merge mode. For the CU coded with normal AMVP mode, either theinteger-pel or quarter-pel motion is used.

SHVC upsampling interpolation filters, which have same filter length andnormalization factor as HEVC motion compensation interpolation filters,are used as motion compensation interpolation filters for the additionalfractional pel positions. The chroma component motion vector accuracy is1/32 sample in the JEM, the additional interpolation filters of 1/32 pelfractional positions are derived by using the average of the filters ofthe two neighbouring 1/16 pel fractional positions.

2.4 Examples of Overlapped Block Motion Compensation (OBMC)

In the JEM, OBMC can be switched on and off using syntax at the CUlevel. When OBMC is used in the JEM, the OBMC is performed for allmotion compensation (MC) block boundaries except the right and bottomboundaries of a CU. Moreover, it is applied for both the luma and chromacomponents. In the JEM, an MC block corresponds to a coding block. Whena CU is coded with sub-CU mode (includes sub-CU merge, affine and FRUCmode), each sub-block of the CU is a MC block. To process CU boundariesin a uniform fashion, OBMC is performed at sub-block level for all MCblock boundaries, where sub-block size is set equal to 4×4, as shown inFIGS. 12A and 12B.

FIG. 12A shows sub-blocks at the CU/PU boundary, and the hatchedsub-blocks are where OBMC applies. Similarly, FIG. 12B shows the sub-Pusin ATMVP mode.

When OBMC applies to the current sub-block, besides current motionvectors, motion vectors of four connected neighboring sub-blocks, ifavailable and are not identical to the current motion vector, are alsoused to derive prediction block for the current sub-block. Thesemultiple prediction blocks based on multiple motion vectors are combinedto generate the final prediction signal of the current sub-block.

Prediction block based on motion vectors of a neighboring sub-block isdenoted as PN, with N indicating an index for the neighboring above,below, left and right sub-blocks and prediction block based on motionvectors of the current sub-block is denoted as PC. When PN is based onthe motion information of a neighboring sub-block that contains the samemotion information to the current sub-block, the OBMC is not performedfrom PN. Otherwise, every sample of PN is added to the same sample inPC, i.e., four rows/columns of PN are added to PC. The weighting factors{¼, ⅛, 1/16, 1/32} are used for PN and the weighting factors {¾, ⅞,15/16, 31/32} are used for PC. The exception are small MC blocks, (i.e.,when height or width of the coding block is equal to 4 or a CU is codedwith sub-CU mode), for which only two rows/columns of PN are added toPC. In this case weighting factors {¼, ⅛} are used for PN and weightingfactors {¾, ⅞} are used for PC. For PN generated based on motion vectorsof vertically (horizontally) neighboring sub-block, samples in the samerow (column) of PN are added to PC with a same weighting factor.

In the JEM, for a CU with size less than or equal to 256 luma samples, aCU level flag is signaled to indicate whether OBMC is applied or not forthe current CU. For the CUs with size larger than 256 luma samples ornot coded with AMVP mode, OBMC is applied by default. At the encoder,when OBMC is applied for a CU, its impact is taken into account duringthe motion estimation stage. The prediction signal formed by OBMC usingmotion information of the top neighboring block and the left neighboringblock is used to compensate the top and left boundaries of the originalsignal of the current CU, and then the normal motion estimation processis applied.

2.5 Examples of Local Illumination Compensation (LIC)

LIC is based on a linear model for illumination changes, using a scalingfactor a and an offset b. And it is enabled or disabled adaptively foreach inter-mode coded coding unit (CU).

When LIC applies for a CU, a least square error method is employed toderive the parameters a and b by using the neighboring samples of thecurrent CU and their corresponding reference samples. FIG. 13 shows anexample of neighboring samples used to derive parameters of the ICalgorithm. Specifically, and as shown in FIG. 13, the subsampled (2:1subsampling) neighbouring samples of the CU and the correspondingsamples (identified by motion information of the current CU or sub-CU)in the reference picture are used. The IC parameters are derived andapplied for each prediction direction separately.

When a CU is coded with merge mode, the LIC flag is copied fromneighboring blocks, in a way similar to motion information copy in mergemode; otherwise, an LIC flag is signaled for the CU to indicate whetherLIC applies or not.

When LIC is enabled for a picture, an additional CU level RD check isneeded to determine whether LIC is applied or not for a CU. When LIC isenabled for a CU, the mean-removed sum of absolute difference (MR-SAD)and mean-removed sum of absolute Hadamard-transformed difference(MR-SATD) are used, instead of SAD and SATD, for integer pel motionsearch and fractional pel motion search, respectively.

To reduce the encoding complexity, the following encoding scheme isapplied in the JEM:

-   -   LIC is disabled for the entire picture when there is no obvious        illumination change between a current picture and its reference        pictures. To identify this situation, histograms of a current        picture and every reference picture of the current picture are        calculated at the encoder. If the histogram difference between        the current picture and every reference picture of the current        picture is smaller than a given threshold, LIC is disabled for        the current picture; otherwise, LIC is enabled for the current        picture.

2.6 Examples of Affine Motion Compensation Prediction

In HEVC, only a translation motion model is applied for motioncompensation prediction (MCP). However, the camera and objects may havemany kinds of motion, e.g. zoom in/out, rotation, perspective motions,and/or other irregular motions. JEM, on the other hand, applies asimplified affine transform motion compensation prediction. FIG. 14shows an example of an affine motion field of a block 1400 described bytwo control point motion vectors V₀ and V₁. The motion vector field(MVF) of the block 1400 can be described by the following equation:

$\begin{matrix}\left\{ \begin{matrix}{v_{x} = {{\frac{\left( {v_{1x} - v_{0_{x}}} \right)}{w}x} - {\frac{\left( {v_{1_{y}} - v_{0_{y}}} \right)}{w}y} + v_{0_{x}}}} \\{v_{y} = {{\frac{\left( {v_{1_{y}} - v_{0_{y}}} \right)}{w}x} + {\frac{\left( {v_{1x} - v_{0_{x}}} \right)}{w}y} + v_{0_{y}}}}\end{matrix} \right. & {{Eq}.\;(\; 1)}\end{matrix}$

As shown in FIG. 14, (v_(0x), v_(0y)) is motion vector of the top-leftcorner control point, and (v_(1x), v_(1y)) is motion vector of thetop-right corner control point. To simplify the motion compensationprediction, sub-block based affine transform prediction can be applied.The sub-block size M×N is derived as follows:

$\begin{matrix}\left\{ \begin{matrix}{M = {clip\ 3\ \left( {4,w,\ \frac{w \times {MvPre}}{\max\left( {{{abs}\;\left( {v_{1x} - v_{0x}} \right)},{{abs}\left( {v_{1y} - v_{0y}} \right)}} \right)}} \right)}} \\{N = {clip\ 3\ \left( {4,h,\ \frac{h \times {MvPre}}{\max\left( {{{abs}\;\left( {v_{2x} - v_{0x}} \right)},{{abs}\left( {v_{2y} - v_{0y}} \right)}} \right)}} \right)}}\end{matrix} \right) & {{Eq}.\mspace{11mu}(2)}\end{matrix}$

Here, MvPre is the motion vector fraction accuracy (e.g., 1/16 in JEM).(v_(2x), v_(2y)) is motion vector of the bottom-left control point,calculated according to Eq. (1). M and N can be adjusted downward ifnecessary to make it a divisor of w and h, respectively.

FIG. 15 shows an example of affine MVF per sub-block for a block 1500.To derive motion vector of each M×N sub-block, the motion vector of thecenter sample of each sub-block can be calculated according to Eq. (1),and rounded to the motion vector fraction accuracy (e.g., 1/16 in JEM).Then the motion compensation interpolation filters can be applied togenerate the prediction of each sub-block with derived motion vector.After the MCP, the high accuracy motion vector of each sub-block isrounded and saved as the same accuracy as the normal motion vector.

2.6.1 Embodiments of the AF_INTER Mode

In the JEM, there are two affine motion modes: AF_INTER mode andAF_MERGE mode. For CUs with both width and height larger than 8,AF_INTER mode can be applied. An affine flag in CU level is signaled inthe bitstream to indicate whether AF_INTER mode is used. In the AF_INTERmode, a candidate list with motion vector pair {(v₀, v₁)|v₀={v_(A),v_(B), v_(C)}, v₁ {v_(D),v_(E)}} is constructed using the neighboringblocks.

FIG. 16 shows an example of motion vector prediction (MVP) for a block1600 in the AF_INTER mode. As shown in FIG. 16, v₀ is selected from themotion vectors of the sub-block A, B, or C. The motion vectors from theneighboring blocks can be scaled according to the reference list. Themotion vectors can also be scaled according to the relationship amongthe Picture Order Count (POC) of the reference for the neighboringblock, the POC of the reference for the current CU, and the POC of thecurrent CU. The approach to select v₁ from the neighboring sub-block Dand E is similar. If the number of candidate list is smaller than 2, thelist is padded by the motion vector pair composed by duplicating each ofthe AMVP candidates. When the candidate list is larger than 2, thecandidates can be firstly sorted according to the neighboring motionvectors (e.g., based on the similarity of the two motion vectors in apair candidate). In some implementations, the first two candidates arekept. In some embodiments, a Rate Distortion (RD) cost check is used todetermine which motion vector pair candidate is selected as the controlpoint motion vector prediction (CPMVP) of the current CU. An indexindicating the position of the CPMVP in the candidate list can besignaled in the bitstream. After the CPMVP of the current affine CU isdetermined, affine motion estimation is applied and the control pointmotion vector (CPMV) is found. Then the difference of the CPMV and theCPMVP is signaled in the bitstream.

2.6.3 Embodiments of the AF_MERGE Mode

When a CU is applied in AF_MERGE mode, it gets the first block codedwith an affine mode from the valid neighboring reconstructed blocks.FIG. 17A shows an example of the selection order of candidate blocks fora current CU 1700. As shown in FIG. 17A, the selection order can be fromleft (1701), above (1702), above right (1703), left bottom (1704) toabove left (1705) of the current CU 1700. FIG. 17B shows another exampleof candidate blocks for a current CU 1700 in the AF_MERGE mode. If theneighboring left bottom block 1801 is coded in affine mode, as shown inFIG. 17B, the motion vectors v₂, v₃ and v₄ of the top left corner, aboveright corner, and left bottom corner of the CU containing the sub-block1701 are derived. The motion vector v₀ of the top left corner on thecurrent CU 1700 is calculated based on v₂, v₃ and v₄. The motion vectorv₁ of the above right of the current CU can be calculated accordingly.

After the CPMV of the current CU v₀ and v₁ are computed according to theaffine motion model in Eq. (1), the MVF of the current CU can begenerated. In order to identify whether the current CU is coded withAF_MERGE mode, an affine flag can be signaled in the bitstream whenthere is at least one neighboring block is coded in affine mode.

2.7 Examples of Pattern Matched Motion Vector Derivation (PMMVD)

The PMMVD mode is a special merge mode based on the Frame-Rate UpConversion (FRUC) method. With this mode, motion information of a blockis not signaled but derived at decoder side.

A FRUC flag can be signaled for a CU when its merge flag is true. Whenthe FRUC flag is false, a merge index can be signaled and the regularmerge mode is used. When the FRUC flag is true, an additional FRUC modeflag can be signaled to indicate which method (e.g., bilateral matchingor template matching) is to be used to derive motion information for theblock.

At the encoder side, the decision on whether using FRUC merge mode for aCU is based on RD cost selection as done for normal merge candidate. Forexample, multiple matching modes (e.g., bilateral matching and templatematching) are checked for a CU by using RD cost selection. The oneleading to the minimal cost is further compared to other CU modes. If aFRUC matching mode is the most efficient one, FRUC flag is set to truefor the CU and the related matching mode is used.

Typically, motion derivation process in FRUC merge mode has two steps: aCU-level motion search is first performed, then followed by a Sub-CUlevel motion refinement. At CU level, an initial motion vector isderived for the whole CU based on bilateral matching or templatematching. First, a list of MV candidates is generated and the candidatethat leads to the minimum matching cost is selected as the startingpoint for further CU level refinement. Then a local search based onbilateral matching or template matching around the starting point isperformed. The MV results in the minimum matching cost is taken as theMV for the whole CU. Subsequently, the motion information is furtherrefined at sub-CU level with the derived CU motion vectors as thestarting points.

For example, the following derivation process is performed for a W×H CUmotion information derivation. At the first stage, MV for the whole W×HCU is derived. At the second stage, the CU is further split into M×Msub-CUs. The value of M is calculated as in Eq. (3), D is a predefinedsplitting depth which is set to 3 by default in the JEM. Then the MV foreach sub-CU is derived.

$\begin{matrix}{M = {\max\left\{ {4,{\min\left\{ {\frac{M}{2^{D}},\frac{N}{2^{D}}} \right\}}} \right\}}} & {{Eq}.\mspace{14mu}(3)}\end{matrix}$

FIG. 18 shows an example of bilateral matching used in the Frame-Rate UpConversion (FRUC) method. The bilateral matching is used to derivemotion information of the current CU by finding the closest matchbetween two blocks along the motion trajectory of the current CU (1800)in two different reference pictures (1810, 1811). Under the assumptionof continuous motion trajectory, the motion vectors MV0 (1801) and MV1(1802) pointing to the two reference blocks are proportional to thetemporal distances, e.g., TD0 (1803) and TD1 (1804), between the currentpicture and the two reference pictures. In some embodiments, when thecurrent picture 1800 is temporally between the two reference pictures(1810, 1811) and the temporal distance from the current picture to thetwo reference pictures is the same, the bilateral matching becomesmirror based bi-directional MV.

FIG. 19 shows an example of template matching used in the Frame-Rate UpConversion (FRUC) method. Template matching can be used to derive motioninformation of the current CU 1900 by finding the closest match betweena template (e.g., top and/or left neighboring blocks of the current CU)in the current picture and a block (e.g., same size to the template) ina reference picture 1910. Except the aforementioned FRUC merge mode, thetemplate matching can also be applied to AMVP mode. In both JEM andHEVC, AMVP has two candidates. With the template matching method, a newcandidate can be derived. If the newly derived candidate by templatematching is different to the first existing AMVP candidate, it isinserted at the very beginning of the AMVP candidate list and then thelist size is set to two (e.g., by removing the second existing AMVPcandidate). When applied to AMVP mode, only CU level search is applied.

The MV candidate set at CU level can include the following: (1) originalAMVP candidates if the current CU is in AMVP mode, (2) all mergecandidates, (3) several MVs in the interpolated MV field (describedlater), and top and left neighboring motion vectors.

When using bilateral matching, each valid MV of a merge candidate can beused as an input to generate a MV pair with the assumption of bilateralmatching. For example, one valid MV of a merge candidate is (MVa,ref_(a)) at reference list A. Then the reference picture ref_(b) of itspaired bilateral MV is found in the other reference list B so thatref_(a) and ref_(b) are temporally at different sides of the currentpicture. If such a ref_(b) is not available in reference list B, ref_(b)is determined as a reference which is different from ref_(a) and itstemporal distance to the current picture is the minimal one in list B.After ref_(b) is determined, MVb is derived by scaling MVa based on thetemporal distance between the current picture and ref_(a), ref_(b).

In some implementations, four MVs from the interpolated MV field canalso be added to the CU level candidate list. More specifically, theinterpolated MVs at the position (0, 0), (W/2, 0), (0, H/2) and (W/2,H/2) of the current CU are added. When FRUC is applied in AMVP mode, theoriginal AMVP candidates are also added to CU level MV candidate set. Insome implementations, at the CU level, 15 MVs for AMVP CUs and 13 MVsfor merge CUs can be added to the candidate list.

The MV candidate set at sub-CU level includes an MV determined from aCU-level search, (2) top, left, top-left and top-right neighboring MVs,(3) scaled versions of collocated MVs from reference pictures, (4) oneor more ATMVP candidates (e.g., up to four), and (5) one or more STMVPcandidates (e.g., up to four). The scaled MVs from reference picturesare derived as follows. The reference pictures in both lists aretraversed. The MVs at a collocated position of the sub-CU in a referencepicture are scaled to the reference of the starting CU-level MV. ATMVPand STMVP candidates can be the four first ones. At the sub-CU level,one or more MVs (e.g., up to 17) are added to the candidate list.

Generation of an interpolated MV field. Before coding a frame,interpolated motion field is generated for the whole picture based onunilateral ME. Then the motion field may be used later as CU level orsub-CU level MV candidates.

In some embodiments, the motion field of each reference pictures in bothreference lists is traversed at 4×4 block level. FIG. 20 shows anexample of unilateral Motion Estimation (ME) 2000 in the FRUC method.For each 4×4 block, if the motion associated to the block passingthrough a 4×4 block in the current picture and the block has not beenassigned any interpolated motion, the motion of the reference block isscaled to the current picture according to the temporal distance TD0 andTD1 (the same way as that of MV scaling of TMVP in HEVC) and the scaledmotion is assigned to the block in the current frame. If no scaled MV isassigned to a 4×4 block, the block's motion is marked as unavailable inthe interpolated motion field.

Interpolation and matching cost. When a motion vector points to afractional sample position, motion compensated interpolation is needed.To reduce complexity, bi-linear interpolation instead of regular 8-tapHEVC interpolation can be used for both bilateral matching and templatematching.

The calculation of matching cost is a bit different at different steps.When selecting the candidate from the candidate set at the CU level, thematching cost can be the absolute sum difference (SAD) of bilateralmatching or template matching. After the starting MV is determined, thematching cost C of bilateral matching at sub-CU level search iscalculated as follows:

C=SAD+w·(|MV _(x) −MV _(x) ^(s)|+|MV _(y) −MV _(y) ^(s)|)  Eq. (4)

Here, w is a weighting factor. In some embodiments, w can be empiricallyset to 4. MV and MV^(s) indicate the current MV and the starting MV,respectively. SAD may still be used as the matching cost of templatematching at sub-CU level search.

In FRUC mode, MV is derived by using luma samples only. The derivedmotion will be used for both luma and chroma for MC inter prediction.After MV is decided, final MC is performed using 8-taps interpolationfilter for luma and 4-taps interpolation filter for chroma.

MV refinement is a pattern based MV search with the criterion ofbilateral matching cost or template matching cost. In the JEM, twosearch patterns are supported—an unrestricted center-biased diamondsearch (UCBDS) and an adaptive cross search for MV refinement at the CUlevel and sub-CU level, respectively. For both CU and sub-CU level MVrefinement, the MV is directly searched at quarter luma sample MVaccuracy, and this is followed by one-eighth luma sample MV refinement.The search range of MV refinement for the CU and sub-CU step are setequal to 8 luma samples.

In the bilateral matching merge mode, bi-prediction is applied becausethe motion information of a CU is derived based on the closest matchbetween two blocks along the motion trajectory of the current CU in twodifferent reference pictures. In the template matching merge mode, theencoder can choose among uni-prediction from list0, uni-prediction fromlist1, or bi-prediction for a CU. The selection ca be based on atemplate matching cost as follows:

  If costBi <= factor * min (cost0, cost1)   bi-prediction is used; Otherwise, if cost0 <= cost1   uni-prediction from list0 is used; Otherwise,   uni-prediction from list1 is used;

Here, cost0 is the SAD of list0 template matching, cost1 is the SAD oflist1 template matching and costBi is the SAD of bi-prediction templatematching. For example, when the value of factor is equal to 1.25, itmeans that the selection process is biased toward bi-prediction. Theinter prediction direction selection can be applied to the CU-leveltemplate matching process.

2.8 Examples of Generalized Bi-Prediction Improvement (GBi)

Generalized Bi-prediction improvement (GBi) is adopted into VTM-3.0. GBiapplies unequal weights to predictors from L0 and L1 in bi-predictionmode. In inter prediction mode, multiple weight pairs including theequal weight pair (½, ½) are evaluated based on rate-distortionoptimization (RDO), and the GBi index of the selected weight pair issignaled to the decoder. In merge mode, the GBi index is inherited froma neighboring CU. The predictor generation formula is shown as inEquation (5).

P _(GBi)=(w0×P _(L0) +w1×P _(L1)+RoundingOffset)>>shiftNum_(GBi)

Herein, P_(GBi) is the final predictor of GBi, w₀ and w₁ are theselected GBi weights applied to predictors (P_(L0) and P_(1L)) of list 0(L0) and list 1 (L1), respectively. RoundingOffset_(GBi) andshiftNum_(GBi) are used to normalize the final predictor in GBi. Thesupported w₁ weight set is {−¼, ⅜, ½, ⅝, 5/4}, in which the five weightscorrespond to one equal weight pair and four unequal weight pairs. Theblending gain, i.e., sum of w₁ and w₀, is fixed to 1.0. Therefore, thecorresponding w₀ weight set is {5/4, ⅝, ½, ⅜, −¼}. The weight pairselection is at CU-level.

For non-low delay pictures, the weight set size is reduced from five tothree, where the w₁ weight set is {⅜, ½, ⅝} and the w₀ weight set is {⅝,½, ⅜}. The weight set size reduction for non-low delay pictures isapplied to the BMS2.1 GBi and all the GBi tests in this contribution.

2.8.1 GBi Encoder Bug Fix

To reduce the GBi encoding time, in current encoder design, the encoderwill store uni-prediction motion vectors estimated from GBi weight equalto 4/8, and reuse them for uni-prediction search of other GBi weights.This fast encoding method is applied to both translation motion modeland affine motion model. In VTM2.0, 6-parameter affine model was adoptedtogether with 4-parameter affine model. The BMS2.1 encoder does notdifferentiate 4-parameter affine model and 6-parameter affine model whenit stores the uni-prediction affine MVs when GBi weight is equal to 4/8.Consequently, 4-parameter affine MVs may be overwritten by 6-parameteraffine MVs after the encoding with GBi weight 4/8. The stored6-parameter affine MVs may be used for 4-parameter affine ME for otherGBi weights, or the stored 4-parameter affine MVs may be used for6-parameter affine ME. The proposed GBi encoder bug fix is to separatethe 4-parameter and 6-parameter affine MVs storage. The encoder storesthose affine MVs based on affine model type when GBi weight is equal to4/8, and reuse the corresponding affine MVs based on the affine modeltype for other GBi weights.

2.8.2 GBi Encoder Speed Up

In this existing implementation, five encoder speed-up methods areproposed to reduce the encoding time when GBi is enabled.

(1) Skipping Affine Motion Estimation for Some GBi Weights Conditionally

In BMS2.1, affine ME including 4-parameter and 6-parameter affine ME isperformed for all GBi weights. We propose to skip affine ME for thoseunequal GBi weights (weights unequal to 4/8) conditionally.Specifically, affine ME will be performed for other GBi weights if andonly if the affine mode is selected as the current best mode and it isnot affine merge mode after evaluating the GBi weight of 4/8. If currentpicture is non-low-delay picture, the bi-prediction ME for translationmodel will be skipped for unequal GBi weights when affine ME isperformed. If affine mode is not selected as the current best mode or ifaffine merge is selected as the current best mode, affine ME will beskipped for all other GBi weights.

(2) Reducing the Number of Weights for RD Cost Checking for Low-DelayPictures in the Encoding for 1-Pel and 4-Pel MVD Precision

For low-delay pictures, there are five weights for RD cost checking forall MVD precisions including ¼-pel, 1-pel and 4-pel. The encoder willcheck RD cost for ¼-pel MVD precision first. We propose to skip aportion of GBi weights for RD cost checking for 1-pel and 4-pel MVDprecisions. We order those unequal weights according to their RD cost in¼-pel MVD precision. Only the first two weights with the smallest RDcosts, together with GBi weight 4/8, will be evaluated during theencoding in 1-pel and 4-pel MVD precisions. Therefore, three weights atmost will be evaluated for 1-pel and 4-pel MVD precisions for low delaypictures.

(3) Conditionally Skipping Bi-Prediction Search when the L0 and L1Reference Pictures are the Same

For some pictures in RA, the same picture may occur in both referencepicture lists (list-0 and list-1). For example, for random access codingconfiguration in CTC, the reference picture structure for the firstgroup of pictures (GOP) is listed as follows.

POC: 16, TL:0, [L0: 0] [L1: 0] POC: 8, TL:1, [L0: 0 16] [L1: 16 0] POC:4, TL:2, [L0: 0 8] [L1: 8 16] POC: 2, TL:3, [L0: 0 4] [L1: 4 8] POC: 1,TL:4, [L0: 0 2] [L1: 2 4] POC: 3, TL:4, [L0: 2 0] [L1: 4 8] POC: 6,TL:3, [L0: 4 0] [L1: 8 16] POC: 5, TL:4, [L0: 4 0] [L1: 6 8] POC: 7,TL:4, [L0: 6 4] [L1: 8 16] POC: 12, TL:2, [L0: 8 0] [L1: 16 8] POC: 10,TL:3, [L0: 8 0] [L1: 12 16] POC: 9, TL:4, [L0: 8 0] [L1: 10 12] POC: 11,TL:4, [L0: 10 8] [L1: 12 16] POC: 14, TL:3, [L0: 12 8] [L1: 12 16] POC:13, TL:4, [L0: 12 8] [L1: 14 16] POC: 15, TL:4, [L0: 14 12] [L1: 16 14]

Note that pictures 16, 8, 4, 2, 1, 12, 14 and 15 have the same referencepicture(s) in both lists. For bi-prediction for these pictures, it ispossible that the L0 and L1 reference pictures are the same. We proposethat the encoder skips bi-prediction ME for unequal GBi weights when 1)two reference pictures in bi-prediction are the same and 2) temporallayer is greater than 1 and 3) the MVD precision is ¼-pel. For affinebi-prediction ME, this fast skipping method is only applied to4-parameter affine ME.

(4) Skipping RD Cost Checking for Unequal GBi Weight Based on TemporalLayer and the POC Distance Between Reference Picture and Current Picture

We propose to skip those RD cost evaluations for those unequal GBiweights when the temporal layer is equal to 4 (highest temporal layer inRA) or the POC distance between reference picture (either list-0 orlist-1) and current picture is equal to 1 and coding QP is greater than32.

(5) Changing Floating-Point Calculation to Fixed-Point Calculation forUnequal GBi Weight During ME

For existing bi-prediction search, the encoder will fix the MV of onelist and refine MV in another list. The target is modified before ME toreduce the computation complexity. For example, if the MV of list-1 isfixed and encoder is to refine MV of list-0, the target for list-0 MVrefinement is modified with Equation (6). O is original signal and P₁ isthe prediction signal of list-1. w is GBi weight for list-1.

T=((O<<3)−w*P ₁)*(1/(8−w))  (6)

Herein, the term (1/(8−w)) is stored in floating point precision, whichincreases computation complexity. We propose to change Equation (6) tofixed-point as in Equation (7).

T=(O*a ₁ −P ₁ *a ₂+round)>>N  (7)

where a₁ and a₂ are scaling factors and they are calculated as:

γ=(1<<N)/(8−w); a ₁ =γ<<3; a ₂=γ*w; round=1<<(N−1)  (7)

2.8.3 CU Size Constraint for GBi

In this method, GBi is disabled for small CUs. In inter prediction mode,if bi-prediction is used and the CU area is smaller than 128 lumasamples, GBi is disabled without any signaling.

2.9 Examples of Bi-Directional Optical Flow (BDOF)

In bi-directional optical flow (BDOF or BIO), motion compensation isfirst performed to generate the first predictions (in each predictiondirection) of the current block. The first predictions are used toderive the spatial gradient, the temporal gradient and the optical flowof each sub-block or pixel within the block, which are then used togenerate the second prediction, e.g., the final prediction of thesub-block or pixel. The details are described as follows.

BDOF is a sample-wise motion refinement performed on top of block-wisemotion compensation for bi-prediction. In some implementations, thesample-level motion refinement does not use signaling.

Let I^((k)) be the luma value from reference k (k=0, 1) after blockmotion compensation, and denote ∂I^((k))/∂x and ∂I^((k))/∂y as thehorizontal and vertical components of the I^((k)) gradient,respectively. Assuming the optical flow is valid, the motion vectorfield (v_(x), v_(y)) is given by:

∂I ^((k)/∂) t+v _(x) ∂I ^((k)/∂) x+v _(y) ∂I ^((k))/∂y=0.  Eq. (5)

Combining this optical flow equation with Hermite interpolation for themotion trajectory of each sample results in a unique third-orderpolynomial that matches both the function values I^((k)) and derivatives∂I^((k))/∂x and ∂I^((k))/∂y at the ends. The value of this polynomial att=0 is the BDOF prediction:

pred_(BIO)=½·(I ⁽⁰⁾⁺ I ⁽¹⁾ +v _(x)/2·(τ₁ ∂ ⁽¹⁾/∂x−τ ₀∂I ^((0)/∂) x)+v_(y)/2·(τ₁ ∂I ⁽¹⁾/∂y−τ ₀ ∂I ^((0)/) ∂ _(y))).  Eq. (6)

FIG. 24 shows an example optical flow trajectory in the Bi-directionalOptical flow (BDOF) method. Here, τ₀ and τ₁ denote the distances to thereference frames. Distances τ₀ and τ₁ are calculated based on POC forRef₀ and Ref₁: τ₀=POC(current)−POC(Ref₀), τ₁=POC(Ref₁)−POC(current). Ifboth predictions come from the same time direction (either both from thepast or both from the future) then the signs are different (e.g.,τ₀·τ₁<0). In this case, BDOF is applied if the prediction is not fromthe same time moment (e.g., τ₀≠τ₁). Both referenced regions havenon-zero motion (e.g., MVx₀, MVy₀, MVx₁, MVy₁≠0) and the block motionvectors are proportional to the time distance (e.g.,MVx₀/MVx₁=MVy₀/MVy₁=−τ₀/τ₁).

The motion vector field (v_(x), v_(y)) is determined by minimizing thedifference Δ between values in points A and B. FIGS. 9A-9B show anexample of intersection of motion trajectory and reference frame planes.Model uses only first linear term of a local Taylor expansion for Δ:

Δ=(I ⁽⁰⁾ −I ⁽¹⁾ ₀ +v _(x)(τ₁ ∂I ⁽¹⁾/∂xτ₀ ∂I ^((0)/∂) x)+v _(y)(τ₁ ∂I⁽¹⁾/∂y+τ₀ ∂I ^((0)/) ∂y))  Eq. (7)

All values in the above equation depend on the sample location, denotedas (i′,j′). Assuming the motion is consistent in the local surroundingarea, Δ can be minimized inside the (2M+1)×(2M+1) square window Ωcentered on the currently predicted point (i,j), where M is equal to 2:

$\begin{matrix}{\left( {v_{x},v_{y}} \right) = {\underset{v_{x\;},v_{y}}{\arg\;\min}{\sum\limits_{{\lbrack{i^{\prime},j}\rbrack} \in \Omega}{\Delta^{2}\left\lbrack {i^{\prime},j^{\prime}} \right\rbrack}}}} & {{Eq}.\mspace{14mu}(8)}\end{matrix}$

For this optimization problem, the JEM uses a simplified approach makingfirst a minimization in the vertical direction and then in thehorizontal direction. This results in the following:

$\begin{matrix}{\mspace{85mu}{v_{x} = {\left( {s_{1} + r} \right) > {{m?{clip}}\; 3\left( {{- {thBIO}},{thBIO},{- \frac{s_{3}}{\left( {s_{1} + r} \right)}}} \right)\text{:}\mspace{11mu} 0}}}} & {{Eq}.\mspace{11mu}(9)} \\{v_{y} = {\left( {s_{5} + r} \right) > {{m?{clip}}\; 3\left( {{- {thBIO}},{thBIO},{- \frac{s_{6} - {v_{x}{s_{2}/2}}}{\left( {s_{5} + r} \right)}}} \right)\text{:}\mspace{11mu} 0}}} & {{Eq}.\mspace{11mu}(10)}\end{matrix}$

where,

$\begin{matrix}{\mspace{79mu}{{{s_{1} = {\sum\limits_{{\lbrack{i^{\prime},j}\rbrack} \in \Omega}\left( {{\tau_{1}{{\partial I^{(1)}}/{\partial x}}} + {\tau_{0}{{\partial I^{(0)}}/{\partial x}}}} \right)^{2}}};}\mspace{20mu}{{s_{3} = {\sum\limits_{{\lbrack{i^{\prime},j}\rbrack} \in \Omega}{\left( {I^{(1)} - I^{(0)}} \right)\left( {{\tau_{1}{{\partial I^{(1)}}/{\partial x}}} + {\tau_{0}{{\partial I^{(0)}}/{\partial x}}}} \right)}}};}{{s_{2} = {\sum\limits_{{\lbrack{i^{\prime},j}\rbrack} \in \Omega}{\left( {{\tau_{1}{{\partial I^{(1)}}/{\partial x}}} + {\tau_{0}{{\partial I^{(0)}}/{\partial x}}}} \right)\left( {{\tau_{1}{{\partial I^{(1)}}/{\partial y}}} + {\tau_{0}{{\partial I^{(0)}}/{\partial y}}}} \right)}}};}\mspace{20mu}{{s_{5} = {\sum\limits_{{\lbrack{i^{\prime},j}\rbrack} \in \Omega}\left( {{\tau_{1}{{\partial I^{(1)}}/{\partial y}}} + {\tau_{0}{{\partial I^{(0)}}/{\partial y}}}} \right)^{2}}};}\mspace{20mu}{s_{6} = {\sum\limits_{{\lbrack{i^{\prime},j}\rbrack} \in \Omega}{\left( {I^{(1)} - I^{(0)}} \right)\left( {{\tau_{1}{{\partial I^{(1)}}/{\partial y}}} + {\tau_{0}{{\partial I^{(0)}}/{\partial y}}}} \right)}}}}} & {{Eq}.\mspace{11mu}(11)}\end{matrix}$

In order to avoid division by zero or a very small value, regularizationparameters r and m can be introduced in Eq. (9) and Eq. (10), where:

r=500·4^(d−8)  Eq. (12)

m=700·4^(d−8)  Eq. (13)

Here, d is bit depth of the video samples.

In order to keep the memory access for BDOF the same as for regularbi-predictive motion compensation, all prediction and gradients values,I^((k)), ∂I^((k))/∂x, ∂I^((k))/∂y, are calculated for positions insidethe current block. FIG. 22A shows an example of access positions outsideof a block 2200. As shown in FIG. 22A, in Eq. (9), (2M+1)×(2M+1) squarewindow Ω centered in currently predicted point on a boundary ofpredicted block needs to accesses positions outside of the block. In theJEM, values of I^((k)), I^((k))/∂x, ∂I^((k))/∂y outside of the block areset to be equal to the nearest available value inside the block. Forexample, this can be implemented as a padding area 2201, as shown inFIG. 22B.

With BDOF, it is possible that the motion field can be refined for eachsample. To reduce the computational complexity, a block-based design ofBDOF is used in the JEM. The motion refinement can be calculated basedon a 4×4 block. In the block-based BDOF, the values of s_(n) in Eq. (9)of all samples in a 4×4 block can be aggregated, and then the aggregatedvalues of s_(n) in are used to derived BDOF motion vectors offset forthe 4×4 block. More specifically, the following formula can used forblock-based BDOF derivation:

$\begin{matrix}{\mspace{20mu}{{{s_{1,b_{k}} = {\sum\limits_{{({x,y})} \in b_{k}}{\sum\limits_{{\lbrack{i^{\prime},j}\rbrack} \in {\Omega{({x,y})}}}\left( {{\tau_{1}{{\partial I^{(1)}}/{\partial x}}} + {\tau_{0}{{\partial I^{(0)}}/{\partial x}}}} \right)^{2}}}};}{{s_{3,b_{k}} = {\sum\limits_{{({x,y})} \in b_{k}}{\sum\limits_{{\lbrack{i^{\prime},j}\rbrack} \in \Omega}{\left( {I^{(1)} - I^{(0)}} \right)\left( {{\tau_{1}{{\partial I^{(1)}}/{\partial x}}} + {\tau_{0}{{\partial I^{(0)}}/{\partial x}}}} \right\}}}}};}{{s_{2,b_{k}} = {\sum\limits_{{({x,y})} \in b_{k}}{\sum\limits_{{\lbrack{i^{\prime},j}\rbrack} \in \Omega}{\left( {{\tau_{1}{{\partial I^{(1)}}/{\partial x}}} + {\tau_{0}{{\partial I^{(0)}}/{\partial x}}}} \right)\left( {{\tau_{1}{{\partial I^{(1)}}/{\partial y}}} + {\tau_{0}{{\partial I^{(0)}}/{\partial y}}}} \right\}}}}};}\mspace{20mu}{{s_{5,b_{k}} = {\sum\limits_{{({x,y})} \in b_{k}}{\sum\limits_{{\lbrack{i^{\prime},j}\rbrack} \in \Omega}\left( {{\tau_{1}{{\partial I^{(1)}}/{\partial y}}} + {\tau_{0}{{\partial I^{(0)}}/{\partial y}}}} \right)^{2}}}};}{s_{6,b_{k}} = {\sum\limits_{{({x,y})} \in b_{k}}{\sum\limits_{{\lbrack{i^{\prime},j}\rbrack} \in \Omega}{\left( {I^{(1)} - I^{(0)}} \right)\left( {{\tau_{1}{{\partial I^{(1)}}/{\partial y}}} + {\tau_{0}{{\partial I^{(0)}}/{\partial y}}}} \right)}}}}}} & {{Eq}.\mspace{14mu}(14)}\end{matrix}$

Here, b_(k) denotes the set of samples belonging to the k-th 4×4 blockof the predicted block. s_(n) in Eq (9) and Eq (10) are replaced by((s_(n,bk))>>4) to derive the associated motion vector offsets.

In some scenarios, MV regiment of BIO may be unreliable due to noise orirregular motion. Therefore, in BDOF, the magnitude of MV regiment isclipped to a threshold value. The threshold value is determined based onwhether the reference pictures of the current picture are all from onedirection. For example, if all the reference pictures of the currentpicture are from one direction, the value of the threshold is set to12×2^(14-d); otherwise, it is set to 12×2^(13-d).

Gradients for BDOF can be calculated at the same time with motioncompensation interpolation using operations consistent with HEVC motioncompensation process (e.g., 2D separable Finite Impulse Response (FIR)).In some embodiments, the input for the 2D separable FIR is the samereference frame sample as for motion compensation process and fractionalposition (fracX, fracY) according to the fractional part of block motionvector. For horizontal gradient ∂I/∂x, a signal is first interpolatedvertically using BIOfilterS corresponding to the fractional positionfracY with de-scaling shift d-8. Gradient filter BIOfilterG is thenapplied in horizontal direction corresponding to the fractional positionfracX with de-scaling shift by 18-d. For vertical gradient ∂I/∂y, agradient filter is applied vertically using BIOfilterG corresponding tothe fractional positionfracY with de-scaling shift d-8. The signaldisplacement is then performed using BIOfilterS in horizontal directioncorresponding to the fractional position fracX with de-scaling shift by18-d. The length of interpolation filter for gradients calculationBIOfilterG and signal displacement BIOfilterF can be shorter (e.g.,6-tap) in order to maintain reasonable complexity. Table 1 shows examplefilters that can be used for gradients calculation of differentfractional positions of block motion vector in BDOF. Table 2 showsexample interpolation filters that can be used for prediction signalgeneration in BIO.

TABLE 1 Exemplary filters for gradient calculations in BDOF (or BIO)Fractional Interpolation filter for pel position gradient (BIOfilterG) 0{8, −39, −3, 46, −17, 5} 1/16 {8, −32, −13, 50, −18, 5} 1/8  {7, −27,−20, 54, −19, 5} 3/16 {6, −21, −29, 57, −18, 5} 1/4  {4, −17, −36, 60,−15, 4} 5/16 {3, −9, −44, 61, −15, 4} 3/8  {1, −4, −48, 61, −13, 3} 7/16{0, 1, −54, 60, −9, 2} 1/2  {−1, 4, −57, 57, −4, 1}

TABLE 2 Exemplary interpolation filters for prediction signal generationin BDOF (or BIO) Fractional pel Interpolation filter for positionprediction signal (BIOfilterS) 0 {0, 0, 64, 0, 0, 0} 1/16 {1, −3, 64, 4,−2, 0} 1/8  {1, −6, 62, 9, −3, 1} 3/16 {2, −8, 60, 14, −5, 1} 1/4  {2,−9, 57, 19, −7, 2} 5/16 {3, −10, 53, 24, −8, 2} 3/8  {3, −11, 50, 29,−9, 2} 7/16 {3, −11, 44, 35, −10, 3} 1/2  {3, −10, 35, 44, −11, 3}

In the JEM, BDOF can be applied to all bi-predicted blocks when the twopredictions are from different reference pictures. When LocalIllumination Compensation (LIC) is enabled for a CU, BDOF can bedisabled.

In some embodiments, OBMC is applied for a block after normal MCprocess. To reduce the computational complexity, BDOF may not be appliedduring the OBMC process. This means that BDOF is applied in the MCprocess for a block when using its own MV and is not applied in the MCprocess when the MV of a neighboring block is used during the OBMCprocess.

2.9.1 Examples of BDOF in VTM-3.0

Step 1: Judge Whether BIO is Applicable (W/H are Width/Height of CurrentBlock)

BIO is not applicable if

-   -   Current video block is affine coded or ATMVP coded

(iPOC−iPOC₀)×(iPOC−iPOC₁)≥0

H==4or (W==4 and H==8)

-   -   with Weighted Prediction    -   GBi weights are not (1,1)

BDOF is not used if total SAD between the two reference blocks (denotedas R₀ and R₁) is smaller than a threshold, wherein

${S\; A\; D} = {\sum\limits_{({x,y})}{{{R_{0}\left( {x,y} \right)} - {R_{1}\left( {x,y} \right)}}}}$

Step 2: Data Preparation

For a W×H block, (W+2)×(H+2) samples are interpolated.

The inner W×H samples are interpolated with the 8-tap interpolationfilter as in normal motion compensation.

The four side outer lines of samples (black circles in FIG. 23) areinterpolated with the bi-linear filter.

For each position, gradients are calculated on the two reference blocks(R₀ and R₁).

Gx0(x, y)=(R0(x+1,y)−R0(x−1, y)>>4

Gy0(x, y)=(R0(x, y+1)−R0(x, y−1 )>>4

Gx1(x, y)=(R1(x+1,y)−R1(x−1, y)>>4

Gy1(x, y)=(R1(x, y+1)−R1(x, y−1 )>>4

For each position, internal values are calculated as:

T1(R0(x, y)>>6)−(R1(x, y)>>6), T2=(Gx0(x,y)+Gx1(x, y)>>3T3=(Gy0(x,y)+Gy1(x, y))>>3; and

B1(x, y)=T2*T2, B2(x, y)=T2*T3, B3(x, y)=−T1*T2, B5(x, y)=T3*T3, B6(x,y)=−T1*T3

Step 3: Calculate Prediction for Each Block

BDOF is skipped for a 4×4 block if SAD between the two 4×4 referenceblocks is smaller than a threshold.

Calculate Vx and Vy.

Calculate the final prediction for each position in the 4×4 block:

b(x, y)=(Vx(Gx ⁰(x,y)−Gx ¹(x,y))+Vy(Gy ⁰(x, y)−Gy ¹(x, y))+1)>>1

P(x, y)=(R ⁰(x, y)+R¹(x, y)+b(x, y)+offset)>>shift

Herein, b(x,y) is known as a correction item.

2.9.2 Alternative Examples of BDOF (or BIO) in VTM-3.0

8.3.4 Decoding Process for Inter Blocks

-   -   If predFlagL0 and predFlagL1 are equal to 1,        DiffPicOrderCnt(currPic,        refPicList0[refIdx0])*DiffPicOrderCnt(currPic,        refPicList1[refIdx1])<0, MotionModelIdc[xCb][yCb] is equal to 0        and MergeModeList[merge_idx[xCb][yCb]] is not equal to SbCol,        set the value of bioAvailableFlag to TRUE.    -   Otherwise, set the value of bioAvailableFlag to FALSE.    -   If bioAvailableFlag is equal to TRUE, the following is applied:    -   The variable shift is set equal to Max(2, 14−bitDepth).    -   The variables cuLevelAbsDiffThres and subCuLevelAbsDiffThres are        set equal to (1<<(bitDepth−8+shift))*cbWidth*cbHeight and        1<<(bitDepth−3+shift). The variable cuLevelSumAbsoluteDiff is        set to 0.    -   For xSbIdx=0 . . . (cbWidth>>2)−1 and ySbIdx=0 . . .        (cbHeight>>2)−1, the variable        subCuLevelSumAbsoluteDiff[xSbIdx][ySbIdx] and the bidirectional        optical flow utilization flag bioUtilizationFlag[xSbIdx][ySbIdx]        of the current subblock are derived as:

subCuLevelSumAbsoluteDiff[xSbIdx][ySbIdx]=ΣiΣjAbs(predSamplesL0L[xSbIdx<<2)+1+i][(ySbIdx<<2)+1+j]−predSamplesL1L[(xSbIdx<<2)+1+i][(ySbIdx<<2)+1+j])with i, j=0 . . 3

bioUtilizationFlag[xSbIdx][ySbIdx]=subCuLevelSumAbsoluteDiff[xSbIdx][ySbIdx]>=subCuLevelAbsDiffThres

cuLevelSumAbsoluteDiff+=subCuLevelSumAbsoluleDiff[xSbldx][ySbldx]

-   -   If cuLevelSumAbsoluteDiff is smaller than cuLevelAbsDiffThres,        set bioAvailableFlag to FALSE.    -   If bioAvailableFlag is equal to TRUE, the prediction samples        inside the current luma coding subblock,        predSamplesL[xL+xSb][yL+ySb] with xL=0 . . . sbWidth−1 and yL=0        . . . sbHeight−1, are derived by invoking the bi-directional        optical flow sample prediction process specified in clause        8.3.4.5 with the luma coding subblock width sbWidth, the luma        coding subblock height sbHeight and the sample arrays        predSamplesL0L and predSamplesL1L, and the variables predFlagL0,        predFlagL1, refIdxL0, refIdxL1.

8.3.4.3 Fractional Sample Interpolation Process

8.3.4.3.1 General

Inputs to this process are:

-   -   a luma location (xSb, ySb) specifying the top-left sample of the        current coding sub-block relative to the top left luma sample of        the current picture,    -   a variable sbWidth specifying the width of the current coding        sub-block in luma samples,    -   a variable sbHeight specifying the height of the current coding        sub-block in luma samples,    -   a luma motion vector mvLX given in 1/16-luma-sample units,    -   a chroma motion vector mvCLX given in 1/32-chroma-sample units,    -   the selected reference picture sample array refPicLXL and the        arrays refPicLXCb and refPicLXCr.    -   the bidirectional optical flow enabling flag bioAvailableFlag.

Outputs of this process are:

-   -   an (sbWidth)×(sbHeight) array predSamplesLXL of prediction luma        sample values when bioAvailableFlag is FALSE, or an        (sbWidth+2)×(sbHeight+2) array predSamplesLXL of prediction luma        samaple values when bioAvailableFlag is TRUE.    -   two (sbWidth/2)×(sbHeight/2) arrays predSamplesLXCb and        predSamplesLXCr of prediction chroma sample values.

Let (xIntL, yIntL) be a luma location given in full-sample units and(xFracL, yFracL) be an offset given in 1/16-sample units. Thesevariables are used only in this clause for specifying fractional-samplelocations inside the reference sample arrays refPicLXL, refPicLXCb andrefPicLXCr.

When bioAvailableFlag is equal to TRUE, for each luma sample location(xL=−1 . . . sbWidth, yL=−1 . . . sbHeight) inside the prediction lumasample array predSamplesLXL, the corresponding prediction luma samplevalue predSamplesLXL[xL][yL] is derived as follows:

-   -   The variables xIntL, yIntL, xFracL and yFracL are derived as        follows:

xIntL=xSb=−1+(mvLX[0]>>4)+xL

yIntl=ySb=−1+(mvLX[1]>>4)+xL

xFracL=mvLX[0]& 15

yFracL=mvLX[1]& 15

-   -   The value of bilinearFiltEnabledFlag is derived as follows:    -   If xL is equal to −1 or sbWidth, or yL is equal to −1 or        sbHeight, set the value of bilinearFiltEnabledFlag to TRUE.    -   Else, set the value of bilinearFiltEnabledFlag to FALSE    -   The prediction luma sample value predSamplesLXL[xL][yL] is        derived by invoking the process specified in clause 8.3.4.3.2        with (xIntL, yIntL), (xFracL, yFracL), refPicLXL and        bilinearFiltEnabledFlag as inputs.

When bioAvailableFlag is equal to FALSE, for each luma sample location(xL=0 . . . sbWidth−1, yL=0 . . . sbHeight−1) inside the prediction lumasample array predSamplesLXL, the corresponding prediction luma samplevalue predSamplesLXL[xL][yL] is derived as follows:

-   -   The variables xIntL, yIntL, xFracL and yFracL are derived as        follows:

xIntL=xSb+(mvLX[0]>>4)+xL

yIntL=ySb+(mvLX[1]>>4)+yL

xFracL=mvLX[0]& 15

yFracL=mvLX[1]& 15

-   -   The variable bilinearFiltEnabledFlag is set to FALSE.    -   The prediction luma sample value predSamplesLXL[xL][yL] is        derived by invoking the process specified in clause 8.3.4.3.2        with (xIntL, yIntL), (xFracL, yFracL), and refPicLXL and        bilinearFiltEnabledFlag as inputs.

8.3.4.5 Bi-Directional Optical Flow (BDOF or BIO) Prediction Process

Inputs to this process are:

-   -   two variables nCbW and nCbH specifying the width and the height        of the current coding block,    -   two (nCbW+2)×(nCbH+2) luma prediction sample arrays        predSamplesL0 and predSamplesL1,    -   the prediction list utilization flags, predFlagL0 and        predFlagL1,    -   the reference indices refIdxL0 and refIdxL1,    -   the bidirectional optical flow utilization flags        bioUtilizationFlag[xSbIdx][ySbIdx] with xSbIdx=0 . . .        (nCbW>>2)−1, ySbIdx=0 . . . (nCbH>>2)−1

Output of this process is the (nCbW)×(nCbH) array pbSamples of lumaprediction sample values.

The variable bitDepth is set equal to BitDepthY.

The variable shift2 is set equal to Max(3, 15−bitDepth) and the variableoffset2 is set equal to 1<<(shift2−1).

The variable mvRefineThres is set equal to 1<<(13−bitDepth).

For xSbIdx=0. . . (nCbW>>2)−1 and ySbIdx=0. . . (nCbH>>2)

-   -   If bioUtilizationFlag[xSbIdx][ySbIdx] is FALSE, for x=xSb . . .        xSb+3, y=ySb . . . ySb+3, the prediction sample values of the        current prediction unit are derived as follows:    -   pbSamples[x][y]=Clip3(0,(1<<bitDepth )−1,    -   (predSamplesL0[x][y]+predSamplesL1[x][y]+offset2)>>shift2)    -   Otherwise, the prediction sample values of the current        prediction unit are derived as follows:    -   The location (xSb, ySb) specifying the top-left sample of the        current subblock relative to the top left sample of prediction        sample arrays predSamplesL0 and predSampleL1 is derived as        follows:

xSb=(xSbIdx>>2)+1

ySb=(ySbIdx>>2)+1

-   -   For x=xSb−1 . . . xSb+4, y=ySb−1 . . . ySb+4, the followings are        applied:    -   The locations (hx, vy) for each of the corresponding sample        (x, y) inside the prediction sample arrays are derived as        follows:

hx=Clip3(1, nCbW, x)

vy=Clip3(1, nCbH, y)

-   -   The variables gradientHL0[x][y], gradientVL0[x][y],        gradientHL1[x][y] and gradientVL1[x][y] are derived as follows:

gradientHL0[x][y]=(predSamplesL0[hx +1][vy]−predSampleL0[hx−1][vy]>>4

gradient VL0[x][y]=(predSampleL0[hx][vy+1]−predSampleL0[hx][vy−1])>>4

gradientHL1[x][y]=(predSamplesL1[hx+1][vy]−predSampleL1[hx−1][vy])>>4

gradientVL1[x][y]=(predSampleL1[hx][vy+1]−predSampleL1[hx][vy−1])>>4

The variables temp, tempX and tempY are derived as follows:

temp[x][y]=(predSamplesL0[hx][vy]>>6)−(predSamplesL1[hx][vy]>>6)

tempX[x][y]=(gradientHL0[x][y]+gradientHL1[x][y])>>3

tempY[x][y]=(gradientVL0[x][y]+gradientVL1[x][y])>>3

The variables sGx2, sGy2, sGxGy, sGxdI and sGydI are derived as follows:

sGx2=Σ_(x)Σ_(y)(tempX[xSb+x][ySb+y]* tempX[xSb+x][ySb+y]) with x,y=−1..4

sGy2=Σ_(x)Σ_(y)(tempY[xSb+x][ySb+y]* tempY[xSb+x][ySb+y]) with x,y=1..4

sGxGy=Σ_(x)Σ_(y)(tempX[xSb+x][ySb+y]* tempY[xSb+x][ySb+y]) with x,y=−1..4

sGxdI=Σ_(x)Σ_(y)(−tempX[xSb+x][ySb+y]* temp[xSb+x][ySb+y]) with x,y=−1..4

sGydI=Σ_(x)Σ_(y)(−tempY[xSb+x][ySb+y]* temp[xSb+x][ySb+y]) with x,y=−1..4

The horizontal and vertical motion refinements of the current sub-blockare derived as:

vx=sGx2>0?Clip3(−mvRefineThres, mvRefineThres,−(sGxdI<<3)>>Floor(Log2(sGx2))):0

vy=sGy2>0?Clip3(−mvRefineThres, mvRefineThres,((sGydI<<3)−((vx*sGxGym)<<12+vx*sGxGys)>>1)>>Floor(Log2(sGy2))):0

sGxGym=sGxGy>>12;

sGxGys=sGxGy & ((1<<12)−1)

For x=xSb−1. . . xSb+2,y=ySb−1. . . ySb+2, the followings are applied:

sampleEnh=Round((vx*(gradientHL1[x+1][y+1]−gradientHL0[x+1][y+1]))>>1)+Round((vy*(gradientVL1[x+1][y+1]-gradientVL0[x+1][y+1]))>>1)

pbSamples[x][y]=Clip3(0, (1<<bitDepth)−1, (predSamplesL0[x+1][y+1]+predSamplesL1[x+1][y+1]+sampleEnh+offset2)>>shift2)

2.10 Examples of Decoder-Side Motion Vector Refinement (DMVR)

In a bi-prediction operation, for the prediction of one block region,two prediction blocks, formed using a motion vector (MV) of list0 and aMV of list1, respectively, are combined to form a single predictionsignal. In the decoder-side motion vector refinement (DMVR) method, thetwo motion vectors of the bi-prediction are further refined by abilateral template matching process. The bilateral template matchingapplied in the decoder to perform a distortion-based search between abilateral template and the reconstruction samples in the referencepictures in order to obtain a refined MV without transmission ofadditional motion information.

In DMVR, a bilateral template is generated as the weighted combination(i.e. average) of the two prediction blocks, from the initial MV0 oflist0 and MV1 of list1, respectively, as shown in FIG. 24. The templatematching operation consists of calculating cost measures between thegenerated template and the sample region (around the initial predictionblock) in the reference picture. For each of the two reference pictures,the MV that yields the minimum template cost is considered as theupdated MV of that list to replace the original one. In the JEM, nine MVcandidates are searched for each list. The nine MV candidates includethe original MV and 8 surrounding MVs with one luma sample offset to theoriginal MV in either the horizontal or vertical direction, or both.Finally, the two new MVs, i.e., MV0′ and MV1′ as shown in FIG. 24, areused for generating the final bi-prediction results. A sum of absolutedifferences (SAD) is used as the cost measure. Please note that whencalculating the cost of a prediction block generated by one surroundingMV, the rounded MV (to integer pel) is actually used to obtain theprediction block instead of the real MV.

DMVR is applied for the merge mode of bi-prediction with one MV from areference picture in the past and another from a reference picture inthe future, without the transmission of additional syntax elements. Inthe JEM, when LIC, affine motion, FRUC, or sub-CU merge candidate isenabled for a CU, DMVR is not applied.

3. Exemplary Embodiments Related to the Disclosed Technology 3.1Examples of Cross-Component Linear Model (CCLM) Prediction

To reduce the cross-component redundancy, a CCLM prediction mode, a.k.a.LM, is used in the JEM, for which the chroma samples are predicted basedon the reconstructed luma samples of the same CU by using a linear modelas follows:

pred _(c)(i,j)=α·rec _(L)l ′(i,j)+β

Herein, pred_(C)(i,j) represents the predicted chroma samples in a CUand reC_(L)′(i,j) represents the downsampled reconstructed luma samplesof the same CU for color formats 4:2:0 or 4:2:2 while rec_(L)′(i,j)represents the reconstructed luma samples of the same CU for colorformat 4:4:4. CCLM Parameters α and β are derived by minimizing theregression error between the neighboring reconstructed luma and chromasamples around the current block as follows:

${\alpha = \frac{{N \cdot {\sum\left( {{L(n)} \cdot {C(n)}} \right)}} - {\sum{{L(n)} \cdot {\sum{C(n)}}}}}{{N \cdot {\sum\left( {{L(n)} \cdot {L(n)}} \right)}} - {\sum{{L(n)} \cdot {\sum{L(n)}}}}}}{\beta = \frac{{\sum{C(n)}} - {\alpha \cdot {\sum{L(n)}}}}{N}}$

Herein, L(n) represents the down-sampled (for color formats 4:2:0 or4:2:2) or original (for color format 4:4:4) top and left neighbouringreconstructed luma samples, C(n) represents the top and leftneighbouring reconstructed chroma samples, and value of Nis equal totwice of the minimum of width and height of the current chroma codingblock. For a coding block with a square shape, the above two equationsare applied directly. For a non-square coding block, the neighbouringsamples of the longer boundary are first subsampled to have the samenumber of samples as for the shorter boundary. FIG. 25 shows thelocation of the left and above reconstructed samples and the sample ofthe current block involved in the CCLM mode.

This regression error minimization computation is performed as part ofthe decoding process, not just as an encoder search operation, so nosyntax is used to convey the a and R values. The CCLM prediction modealso includes prediction between the two chroma components, i.e., the Crcomponent is predicted from the Cb component. Instead of using thereconstructed sample signal, the CCLM Cb-to-Cr prediction is applied inresidual domain. This is implemented by adding a weighted reconstructedCb residual to the original Cr intra prediction to form the final Crprediction:

pred _(Cr) ^(*)(i,j)+α·resi _(Cb)′(i,j)

Herein, resi_(Cb)′(ij) presents the reconstructed Cb residue sample atposition (i,j).

The scaling factor α is derived in a similar way as in the CCLMluma-to-chroma prediction. The only difference is an addition of aregression cost relative to a default α value in the error function sothat the derived scaling factor is biased towards a default value of−0.5 as follows:

$\alpha = \frac{{N \cdot {\sum\left( {{{Cb}(n)} \cdot {{Cr}(n)}} \right)}} - {\sum{{{Cb}(n)} \cdot {\sum{{Cr}(n)}}}} + {\lambda \cdot \left( {- 0.5} \right)}}{{N \cdot {\sum\left( {{{Cb}(n)} \cdot {{Cb}(n)}} \right)}} - {\sum{{{Cb}(n)} \cdot {\sum{{Cb}(n)}}}} + \lambda}$

Herein, Cb(n) represents the neighbouring reconstructed Cb samples,Cr(n) represents the neighbouring reconstructed Cr samples, and A isequal to Σ(Cb(n)·Cb(n))>>9.

The CCLM luma-to-chroma prediction mode is added as one additionalchroma intra prediction mode. At the encoder side, one more RD costcheck for the chroma components is added for selecting the chroma intraprediction mode. When intra prediction modes other than the CCLMluma-to-chroma prediction mode is used for the chroma components of aCU, CCLM Cb-to-Cr prediction is used for Cr component prediction.

3.2 Examples of Simplified CCLM

In some existing implementations, the LMS algorithm of the linear modelparameters a and R is replaced by a straight line equation. The 2 points(couple of Luma and Chroma) (A, B) are the minimum and maximum valuesinside the set of neighboring Luma samples as depicted in FIG. 26.Herein, the linear model parameters a and R are obtained according to:

${\alpha = \frac{y_{B} - y_{A}}{x_{B} - x_{A}}},{{{and}\mspace{14mu}\beta} = {y_{A} - {\alpha{x_{A}.}}}}$

The division may be avoided and replaced by a multiplication and ashift.

To derive the Chroma predictor, as for the current VTM implementation,the multiplication is replaced by an integer operation as the following:

pred_(C)(i, j) = (A ⋅ rec_(L)^(′(i, j)))>> S  + β

Yet the implementation is also simpler t an t e current VTMimplementation because shift S always has the same value. In term ofcomplexity, the proposed algorithm reduces the number of operations asdepicted in the following table:

Number of operations Number of operations Operations (LMS) (disclosedtechnology) Multiplications 2N + 2 + 2 1 Sums 7N + 3 3 “Divisions” 2 1Comparisons 2N

The number of operations is reduced but the proposed method needsseveral comparisons to obtain the minimum and maximum luma values of theneighboring sample.

4. Drawbacks of Existing Implementations

Some existing implementations, suffer from the following drawbacks:

-   -   (1) BIO and CCLM both need linear regression but in different        simplified ways.    -   (2) BIO implementation in VTM does not consider the POC        distance.    -   (3) The velocities on Ref 0 are denoted as v_(x) ⁽⁰⁾ and v_(y)        ⁽⁰⁾. The velocities on Ref 1 are denoted as v_(x) ⁽¹⁾ and v_(y)        ⁽¹⁾. In the current BIO design, it is assumed that the        velocities on the two reference pictures are mirrored such that        v_(x) ⁽¹⁾=−v_(x) ⁽⁰⁾=v_(x) and v_(y) ⁽¹⁾=−v_(y) ⁽⁰⁾=v_(y) (or        v_(x) ⁽¹⁾=v_(x) ⁽⁰⁾=v_(x) and v_(y) ⁽¹⁾=v_(y) ⁽⁰⁾=v_(y)).        However, this assumption may not be true.    -   (4) The derivation of velocities v_(x) and v_(y) in BIO is        over-simplified.    -   (5) The derivation of v_(x) and v_(y) in BIO can be combined        with DMVR or decoder side motion derivation.    -   (6) Knowing v_(x) and v_(y), predictions/gradients on Ref0/Ref1        may be further refined.    -   (7) An additional interpolation filter (bi-linear filter) is        introduced to interpolate pixels and boundaries.    -   (8) Some rounding and clipping operations are missing in BIO        procedure.    -   (9) Precisions of internal operations can be further lowered.    -   (10) The SAD for BIO calculation can be simplified.    -   (11) When GBi is applied, the weighting values are not        considered appropriately in the BIO derivation procedure.        5. Example Methods for Inter Prediction with Refinement

Embodiments of the presently disclosed technology overcome the drawbacksof existing implementations, harmonize the linear regression procedurein BIO and CCLM and propose BIO modifications, thereby providing videocoding with higher coding efficiencies. The harmonization of linear modeprediction, based on the disclosed technology, may enhance both existingand future video coding standards, is elucidated in the followingexamples described for various implementations. The examples of thedisclosed technology provided below explain general concepts, and arenot meant to be interpreted as limiting. In an example, unlessexplicitly indicated to the contrary, the various features described inthese examples may be combined.

Denote reference picture of current picture from list 0 and list 1 byRef0 and Ref1 respectively, denote τ₀=POC(current)−POC(Ref0),τ₁=POC(Ref1)−POC(current), and denote reference block of the currentblock from Ref0 and Ref1 by refblk0 and refblk1 respectively. For asubblock in current block, MV of its corresponding subblock in refblk0pointing to refblk1 is denoted by (v_(x), v_(y)). MVs of the subblock inRef0 and Ref1 are denoted by (mvL0_(x), mvL0_(y)) and (mvL1_(x),mvL1_(y)) respectively.

Shift(x, s) is defined as Shift(x,s)=(x+off)>>s

SignShift(x, s) is defined as

${SignShift}{\left( {x,s} \right) = \left\{ \begin{matrix}{\left( {x + {off}} \right)\operatorname{>>}s} & {x \geq 0} \\{- \left( {\left( {{- x} + {off}} \right)\operatorname{>>}s} \right)} & {x < 0}\end{matrix} \right.}$

Herein, off represent an offset that is an integer, e.g., 0 or 2^(s-1).

Clip3(x, min, max) is defined as

${{Clip}\; 3\left( {{mi},{ma},x} \right)} = \left\{ \begin{matrix}{mi} & {x < {mi}} \\x & {{mi} \leq x \leq {ma}} \\{ma} & {x > {ma}}\end{matrix} \right.$

Example 1. It is proposed that the linear optimization module used inBIO can be used by other coding tools in a video coding system.

-   -   (a) The linear optimization module involves a group of samples        (u_(k), v_(k), w_(k)) with k from 0 to N−1 as input, two        parameters a and b as output, so that Σ_(k=0)        ^(N-1)(u_(k)+a×v_(k)+b×w_(k))² can be minimized, or        approximately minimized. From Eq. (9) and Eq. (10), it is known        that in BIO:

u _(k) =I ⁽⁰⁾(x,y)−I ⁽¹⁾(x,y)

v _(k) =G _(x) ⁽⁰⁾(x,y)+G _(x) ⁽¹⁾(x,y)

w _(k) =G _(y) ⁽⁰⁾(x,y)+G _(y) ⁽¹⁾(x,y)

a=v _(x)

b=v _(y)

Herein, G_(x)(x,y) and G_(y)(x,y) represent horizontal and verticalgradients respectively.

-   -   (b) In one example, the linear optimization module used in BIO        is used to derive the parameters for CCLM. From Eq. (18),        Eq.(19), the notations can be written as:

u _(k) =−C(n)

v _(k) =L(n)

w _(k)=1

a=α

b=β

-   -   (c) Alternatively, the linear optimization module used in        another coding tool such as CCLM can be used to derive v_(x) and        v_(y) in BIO.

Example 2. It is proposed that whether to and how to apply BIO proceduredepends on POC distances.

-   -   (a) BIO procedure is not applied if abs(τ₀)≥T0 or abs(τ₁)≥T1. T0        and T1 are integers, e.g. T0=T1=4. T0 and T1 can be fixed        numbers or signaled from the encoder to the decoder in        VPS/SPS/PPS/slice header/tile group header/tile/CTU/CU.    -   (b) BIO procedure is not applied if abs(τ₀)≥T0 and abs(τ₁)≥T1.        T0 and T1 are integers, e.g. T0=T1=4. T0 and T1 can be fixed        numbers or signaled from the encoder to the decoder in        VPS/SPS/PPS/slice header/tile group header/tile/CTU/CU.    -   (c) BIO procedure is not applied if abs(τ₀)+abs(τ₁)≥T. T is an        integer, e.g. T=8. T can be a fixed number or signaled from the        encoder to the decoder in VPS/SPS/PPS/slice header/tile group        header/tile/CTU/CU.    -   (d) BIO procedure is not applied if abs(abs(τ₀)−abs(τ₁))≥T. T is        an integer, e.g. T=8. T can be a fixed number or signaled from        the encoder to the decoder in VPS/SPS/PPS/slice header/tile        group header/tile/CTU/CU.    -   (e) BIO procedure is not applied if abs(τ₀)≥T*abs(τ₁) or        abs(τ₁)≥T*abs(τ₀). T is an integer, e.g. T=4. T can be a fixed        number or signaled from the encoder to the decoder in        VPS/SPS/PPS/slice header/tile group header/tile/CTU/CU.

Example 3. It is proposed that the velocity vectors used to refine theprediction value in the BIO process depend on POC distances. Theprediction after BIO procedure is calculated as

Pred_(BIO)=1/2·(I ⁽⁰⁾ +I ^((1)+v) _(x) ⁽¹⁾/2·G _(x) ⁽¹⁾ +v _(y) ⁽¹⁾/2·G_(y) ⁽¹⁾ +v _(x) ⁽⁰⁾/2·G _(x) ⁽⁰⁾ +v _(y) ⁽⁰⁾/2·G _(y) ⁽⁰⁾)  (22)

where G⁽⁰⁾ _(x) and G⁽⁰⁾ _(y) represent the horizontal and verticalgradients on Ref0 and G⁽⁰⁾ _(x) and G⁽⁰⁾ _(y) represent the horizontaland vertical gradients on Ref0.

-   -   (i) In the BIO design in VTM-3.0, v_(x)=v_(x) ⁽¹⁾=−v_(x) ⁽⁰⁾ and        v_(y)=v_(y) ⁽¹⁾=−v_(y) ⁽⁰, where v_(x) and v_(y) are derived.        Then Eq. (22) is identical to Eq. (9).    -   (ii) In one example

${{v_{x}^{(1)} = {v_{x}\frac{2{\tau_{0}}}{{\tau_{0}} + {\tau_{1}}}}},{v_{x}^{(0)} = {{- v_{x}}\frac{2{\tau_{1}}}{{\tau_{0}} + {\tau_{1}}}}}}{{v_{y}^{(1)} = {v_{y}\frac{2{\tau_{0}}}{{\tau_{0}} + {\tau_{1}}}}},{v_{y}^{(0)} = {{- v_{y}}\frac{2{\tau_{1}}}{{\tau_{0}} + {\tau_{1}}}}}}$

Herein, the divisions can be implemented as multiplication and shift asMV scaling operation in HEVC.

Example 4. Instead of always firstly assuming v_(y) equal to 0 to derivev_(x), it is proposed to firstly derive v_(y) and based on v_(y) toderive v_(x). Alternatively, whether to derive v_(y) or v_(x) firstlymay depend on coded information, e.g., values of the horizontal andvertical components of the motion vectors, block sizes, etc.

Example 5. It is proposed that v_(x) and v_(y) are derived in aniterative way.

-   -   a. In one example, v_(x) derived in the i th step is used to        derive v_(y) in the i th step, and the derived v_(y) in the i th        step is used to derive v_(x) derived in the i+1 th step. The        derivation is done iteratively.    -   b. Alternatively, v_(y) derived in the i th step is used to        derive v_(x) in the i th step, and the derived v_(x) in the i th        step is used to derive v_(y) derived in the i+1 th step. The        derivation is done iteratively.    -   c. Whether to use v_(x) to derive v_(y) or v_(y) to derive v_(x)        in the same step may depend on the coded information, such as        values of the horizontal and vertical components of the motion        vectors, block sizes, etc. al.    -   d. In one example,

${v_{y} = {- \frac{s_{6} - {ɛ \times v_{x}s_{2}}}{s_{5}}}},$

-   -    where s2, s5, s6 are defined as in (13). ε is an integer or        fractional number such as ½ or ¼.        -   i. The division operation can be simplified, e.g. replacing            by the MSB shift as in JEM-3.0.        -   ii. Clipping operation may be after the division operation.    -   e. In one example

${v_{x} = {- \frac{s_{3} - {ɛ \times v_{y}s_{2}}}{s_{1}}}},$

-   -    where s1 s2 s3 are defined as in (13). ε is an integer or        fractional number such as ½ or ¼.        -   i. The division operation can be simplified, e.g. replacing            by the MSB shift as in JEM-3.0.        -   ii. Clipping operation may be after the division operation.    -   f. The derivation is done iteratively until i reaches a prefixed        number such as 2.    -   g. Alternatively, the derivation is done iteratively until the        absolute difference between v_(x) and/or v_(y) before and after        one round of derivation is smaller than a threshold.

Example 6. The derivation of v_(x) and v_(y) in BIO can be combined withDMVR, bilateral matching or other decoder side motion derivationmethods.

-   -   a. v_(x) and v_(y) derived in BIO for a block or sub-block (such        as a 4×4 block) can be used to derive the search start-point of        DMVR, bilateral matching or other decoder side motion derivation        methods. Suppose the original MVs for Ref0 and Ref1 is MV0 and        MV1, the MVs noted as MV0′ and MV1′ as the start-point of DMVR,        bilateral matching or other decoder side motion derivation        methods can be calculated as:        -   i. in one example, MV0′=MV0+(v_(x), v_(y)), MV1′=MV1−(v_(x),            v_(y));        -   ii. in one example, MV0′=MV0−(v_(x), v_(y)),            MV1′=MV1+(v_(x), v_(y));        -   iii. in one example, MV0′=MV0+Scale(v_(x), v_(y)),            MV1′=MV1−Scale (v_(x), v_(y));        -   iv. in one example, MV0′=MV0−Scale (v_(x), v_(y)),            MV1′=MV1+Scale (v_(x), v_(y));        -   v. in one example, MV0′=Clip(MV0+Scale(v_(x), v_(y))),            MV1′=Clip(MV1−Scale (v_(x), v_(y)));        -   vi. in one example, MV0′=Clip(MV0−Scale (v_(x), v_(y))),            MV1′=Clip(MV1+Scale (v_(x), v_(y)));    -   b. Alternatively, the output MVs of DMVR, bilateral matching or        other decoder side motion derivation methods for a block or        sub-block (such as a 4×4 block) can be used to derive v_(x) and        v_(y) used in BIO. Suppose the original MVs for Ref0 and Ref1 is        MV0 and MV1, the output MVs are MV0′ and MV1′, then v_(x) and        v_(y) can be calculated as    -   i. in one example, (v_(x), v_(y))=Scale (MV0′−MV0);    -   ii. in one example, (v_(x), v_(y))=Scale (MV0−MV0′);    -   iii. in one example, (v_(x), v_(y))=Scale (MV1′−MV1);    -   iv. in one example, (v_(x), v_(y))=Scale (MV1−MV1′);    -   v. in one example, (v_(x), v_(y))=(Scale (MV0′−MV0)+Scale        (MV1′−MV1))/2;    -   vi. in one example, (v_(x), v_(y))=(Scale (MV0−MV0′)+Scale        (MV1−MV1′))/2;

Example 7. It is proposed that the derived v_(x) and v_(y) can be usedto refine the predictions and gradients on Ref0 and Ref1. Then therefined predictions and gradients are used to derive new v_(x) andv_(y).

-   -   a. The derivation and refinement procedure can be done        iteratively until the absolute difference between v_(x) or v_(y)        before and after one round of derivation is smaller than a        threshold. Alternatively, the derivation and refinement        procedure can be done iteratively until the iteration reaches a        predefined number of times. For example, the number is 2.    -   b. Suppose the original MVs for Ref0 and Ref1 is MV0 and MV1,        the MVs noted as MV0′ and MV1′ to get the refined predictions        and gradients can be calculated as        -   i. in one example, MV0′=MV0+(v_(x), v_(y)), MV1′=MV1-(v_(x),            v_(y));        -   ii. in one example, MV0′=MV0−(v_(x), v_(y)),            MV1′=MV1+(v_(x), v_(y));        -   iii. in one example, MV0′=MV0+Scale(v_(x), v_(y)),            MV1′=MV1−Scale (v_(x), v_(y));        -   iv. in one example, MV0′=MV0−Scale (v_(x), v_(y)),            MV1′=MV1+Scale (v_(x), v_(y));        -   v. in one example, MV0′=Clip(MV0+Scale(v_(x), v_(y))),            MV1′=Clip(MV1−Scale (v_(x), v_(y)));        -   vi. in one example, MV0′=Clip(MV0−Scale (v_(x), v_(y))),            MV1′=Clip(MV1+Scale (v_(x), v_(y)));

Example 8. It is proposed that BIO may be applied to partial sampleswithin one block or one sub-block. In one example, for samples locatedat the first/last row/first/last column, BIO is not applied.

Example 9. It is proposed that only one kind of interpolation filter isused for one color component when BIO is applied.

-   -   a. In one example, only the 8-tap interpolation filter is used        on the luma component when BIO is applied.    -   b. Alternatively, furthermore, to reduce the memory bandwidth,        it is proposed to restrict the size/range of samples to be        fetched less than that required by interpolation filters.    -   c. Alternatively, furthermore, padding may be applied if some        samples are located at positions outside a given size/range.    -   d. Suppose the current block is M×N, the required size of        sub-samples is (M+G)×(N+G), the required size by L-tap        interpolation filter should be (M+G+L−1)×(N+G+L−1). It is        proposed to the allowed size of integer luma samples to be        fetched with the BIO procedure is (M+L−1+k)×(N+L−1+k). e.g., k        is 0, or 1 and k is smaller than G. If an integer luma sample is        required by the interpolation process but is not allowed to be        fetched, then it will be padded by an adjacent luma sample.        -   i. FIG. 27 shows an example of interpolation filtering in            the BIO procedure. The current block size is M×N, M=N=8 in            the example. The required size of sub-samples is (M+2)×(N+2)            due to gradient calculation. So the integer samples required            by the interpolation filter is (M+2+7)×(N+2+7) which is            equal to 17×17 in the example. However, only (M+7)×(N+7),            which is equal to 15×15 in the example, integer samples are            required to be fetched. Other samples (black circles in the            filter) required by the interpolation filter are padded by            the adjacent fetched samples.    -   e. In one example, the gradients of positions (x,y) where x=−1        or y=−1 or x=W or y=H are not calculated and BIO is not applied        on these positions. Suppose the top-left of the current block is        (0,0) and the width/height of the current block is W/H.    -   f. In one example, the gradients of positions (x,y) where x=−1        or y=−1 or x=W or y=H are calculated in a different way. For        example,

-   gradientHL0[x][y]=

-   (predSamplesL0[hx+1][vy]−predSampleL0[hx][vy]>>4 if hx==1l,

-   (predSamplesL0[hx][vy]−predSampleL0[hx−1][vy]>>4 if hx==nCbW,

-   (predSamplesL0[hx+1][vy]−predSampleL0[hx−1][vy]>>4 Otherwise,

-   gradientVL0[x][y]=

-   (predSampleL0[hx][vy+1]−predSampleL0[hx][vy]>>4 if vy==1,

-   (predSampleL0[hx][vy]−predSampleL0[hx][vy−1])>>4 if vy==nCbH.

-   (predSampleL0[hx][vy+1]−predSampleL0[hx−1][vy]>>4 otherwise.

-   gradientHL1[x][y]=

-   (predSamplesL1[hx+1][vy]−predSampleL1[hx][vy]>>4 if hx==1,

-   (predSamplesL1[hx][vy]−predSampleL1[hx−1][vy]>>4 if hx==nCbW.

-   (predSamplesL0[hx+1][vy]−predSampleL1[hx−1][vy])>>4 Otherwise.

-   gradientVL1[x][y]=

-   (predSampleL1[hx][vy+1]−predSampleL1[hx][vy])>>4 if vy==1.

-   (predSampleL1[hx][vy]−predSampleL1[hx][vy−1])>>4 if vy==nCbH.

-   (predSampleL1[hx][vy+1]−predSampleL1[hx][vy−1])>>4 otherwise.

In another example,

-   gradientHL0[x][y]=-   (predSamplesL0[hx+1][vy]−predSampleL0[hx][vy])>>3 if hx==1,-   (predSamplesL0[hx][vy]−predSampleL0[hx−1][vy])>>3 if hx==nCbW.-   (predSamplesL0[hx+1][vy]−predSampleL0[hx−1][vy])>>4 Otherwise.-   gradientVL0[x][y]=-   (predSampleL0[hx][vy+1]−predSampleL0[hx][vy])>>3 if vy==1,-   (predSampleL0[hx][vy]−predSampleL0[hx][vy−1])>>if vy==nCbH.-   (predSampleL0[hx][vy+1]−predSampleL0[hx][vy−1])>>4 otherwise,-   gradientHL1[x][y]=-   (predSamplesL1[hx+1][vy]−predSampleL1[hx][vy])>>3 if hx==1.-   (predSamplesL1[hx][vy]−predSampleL1[hx−1][vy])>>3 if hx==nCbW,-   (predSamplesL0[hx+1][vy]−predSampleL1[hx−1][vy])>>4 Otherwise,-   gradientVL1[x][y]=-   (predSampleL1[hx][vy+1]−predSampleL1[hx][vy])>>3 if vy==1,-   (predSampleL1[hx][vy]−predSampleL1[hx][vy−1])>>3 if vy==nCbH,-   (predSampleL1[hx][vy+1]−predSampleL1[hx][vy−1])>>4 otherwise.    -   g. In one example, before calculating gradients, the outer        samples (black circles in FIG. 23) are not interpolated by        padded.        -   i. For example, predSampleL0[0][vy]=predSampleL0[1][vy],        -   predSampleL0[nCbW+1][vy]=predSampleL0[nCbW][vy],        -   predSampleL0[hx][0]=predSampleL0[hx][1],        -   predSampleL0[hx][nCbH+1]=predSampleL0[hx][nCbH] and        -   predSampleL1[0][vy]=predSampleL1[1][vy],        -   predSampleL1[nCbW+1][vy]=predSampleL1[nCbW][vy],        -   predSampleL1[hx][0]=predSampleL1[hx][1],        -   predSampleL1[hx][nCbH+1]=predSampleL1[hx][nCbH] for all            valid hx and vy.    -   h. In one example, the gradients calculation method in BIO and        the gradients calculation method in Adaptive Loop Filter (ALF)        is the same method.        -   i. In one example, the gradients calculation method for BIO            in VTM-3 is also used to calculate gradients for ALF.        -   ii. In one example, the gradients calculation method for ALF            in VTM-3 is also used to calculate gradients for BIO.

Example 10. Following changes are proposed to the Bi-directional opticalflow prediction process.

-   -   a. Gradient values may be shifted by a different value other        than 4. In one example, the variables gradientHL0[x][y],        gradientVL0[x][y], gradientHL1[x][y] and gradientVL1[x][y] are        derived as follows:

gradient HL0[x][y]=SignShift(predSamplesL0[hx+1][vy]−predSampleL0[hx−1][vy], S)

gradientVL0[x][y]=SignShift (predSampleL0[hx][vy+1]−predSampleL0[hx][vy−1], S)

gradientHL1[x][y]=SignShift(predSamplesL1[hx+1][vy]−predSampleL1[hx−1][vy], S)

gradientVL1[x][y]=SignShift(predSampleL1[hx][vy+1−predSampleL1[hx]vy−1], S)

-   -   -   i. In one example, S is a fixed number such as 4 or 5.        -   ii. In one example, S depends on the sample bit-depth. For            example, S is equal to B-P where B is the sample bit-depth            such as 8, 10 or 12, and P is an integer such as 6.

    -   b. Gradient values shall be within a range. In one example, the        variables gradientHL0[x][y], gradientVL0[x][y],        gradientHL1[x][y] and gradientVL1[x][y] should be guaranteed be        represented by a K-bit integer, e.g. K=8 or 16.        -   i. For example, after the derivation, the gradients are            clipped as

gradientHL0[x][y]=Clip3(−2^(K−1), 2^(K−1)1, gradientHL0[x][y])

gradientVL0[x][y]=Clip3(−2^(K−1), 2^(K−1)1, gradientVL0[x][y])

gradientHL1[x][y]=Clip3(−2^(K−1), 2^(K−1)1, gradientHL1[x][y])

gradientVL1[x][y]=Clip3(−2^(K−1), 2^(K−1)1, gradientVL1[x][Y])

-   -   c. The internal variables temp, tempX and tempY are derived as        follows:

temp[x][y]=SignShift(predSamplesL0[hx][vy]−predSamplesL1[hx][vy], S1)

tempX[x][y]=SignShift(gradientHL0[x][y]+gradientHL1[x][y], S2)

tempY[x][y]=SignShift(gradientVL0[x][y]+gradientVL1[x][y], S3)

-   -   -   i. In one example, S1, S2 and S3 are fixed numbers such as            S1=6, S2=S3=3.        -   ii. In one example, S1, S2 and S3 depend on the sample            bit-depth. For example, S1=B−P1, S2=B−P2 and S3=B−P3 where B            is the sample bit-depth such as 8, 10 or 12, and P1, P2 and            P3 are integers, e.g. P1=4, P2=P3=7.

    -   d. The internal variables temp, tempX and tempY should be        guaranteed be represented by a K1-bit integer, a K2-bit integer        and a K3-bit integer, e.g. K1=8 or 16, K2=8 or 16, K3=8 or 16.        -   i. For example, the internal variables are clipped after            being derived as

temp [x][y]=Clip3(−2^(K1−1), 2^(K1−1)−1, gradientHL0[x][y])

tempX[x][y]=Clip3(−2^(K2−1), 2^(K2−1)−1, gradientVL0[x][y])

tempY[x][y]=Clip3(−2^(K3−1), 2^(K3−1)−1, gradientHL1[x][y])

-   -   e. The internal variables sGx2, sGy2, sGxGy, sGxdI and sGydI        should be shall be within a range. In one example, these        variables shall be guaranteed be represented by a K1-bit        integer, a K2-bit integer, a K3-bit integer, a K4-bit integer        and a K5-bit integer, e.g. K1=8 or 16, K2=8 or 16, K3=8 or 16.        -   i. For example, the internal variables are right shifted            after being derived as

sGx2=Shift(sGx2, S1)

sGy2=Shift(sGy2, S2)

sGxGy=SignShift (sGxGy, S3)

sGxdI=SignShift (sGxdI, S4)

sGydI=SignShift (sGydI, S5)

-   -   -   In one example, S1, S2, S3, S4 and S5 are fixed numbers such            as 4 or 5.        -   Alternatively, S1, S2, S3, S4 and S5 depends on the sample            bit-depth. For example, S1=B−P1, S2=B−P2, S3=B−P3, S4=B−P4            and S5=B−P5 where B is the sample bit-depth such as 8, 10 or            12, and P1, P2, P3, P4 and P5 are integers.        -   ii. For example, the internal variables are clipped after            being derived as

sGx2=Clip3(0, 2^(K1)−1, sGx2)

sGy2=Clip3(0, 2^(K2)−1, sGy2)

sGxGy=Clip3(−2^(K3−1), 2^(K3−1)−1, sGxGy)

sGxdI=Clip3(−2^(K4−1), 2^(K4−1)−1, sGxdI)

sGydI=Clip3(−2^(K5−1), 2^(K5−1)−1, sGydI)

-   -   f. The variables gradientHL0[x][y], gradientVL0[x][y],        gradientHL1[x][y] and gradientVL1[x][y] are derived only for        selected positions.        -   i. In one example, they are only calculated for samples at            position (x,y) with x % Q==0. For example, Q=2        -   ii. In one example, they are only calculated for samples at            position (x,y) with x % Q==1. For example, Q=2        -   iii. In one example, they are only calculated for samples at            position (x,y) with y % Q==0. For example, Q=2        -   iv. In one example, they are only calculated for samples at            position (x,y) with y % Q==1. For example, Q=2        -   v. In one example, they are only calculated for samples at            position (x,y) with y % Q==0 or y % Q==3. For example, Q=4;    -   g. The internal variables temp[x][y], tempX[x][y] and        tempY[x][y] are only derived for selected positions:        -   i. In one example, they are only calculated for samples at            position (x,y) with x % Q==0. For example, Q=2        -   ii. In one example, they are only calculated for samples at            position (x,y) with x % Q==1. For example, Q=2        -   iii. In one example, they are only calculated for samples at            position (x,y) with y % Q==0. For example, Q=2        -   iv. In one example, they are only calculated for samples at            position (x,y) with y % Q==1. For example, Q=2        -   v. In one example, they are only calculated for samples at            position (x,y) with y % Q==0 or y % Q==3. For example, Q=4;        -   vi. In one example, temp[x][y], tempX[x][y], tempY[x][y] and            gradientHL0[x][y], gradientVL0[x][y], gradientHL1[x][y],            gradientVL1[x][y] are calculated for samples at the same            positions, for example, positions in Examples g.i-g.v.    -   h. The internal variables sGx2, sGy2, sGxGy, sGxdI and sGydI are        calculated only with accumulation of samples on selected        positions. In a formulation way

sGx2=Σ_(x)Σ_(y)(tempX[xSb+x][ySb+y]*tempX[xSb+x][ySb+y]) with x, y ∈ S

sGy2=Σ_(x)Σ_(y)(tempY[xSb+x][ySb+y]*tempY[xSb+x][ySb+y]) with x, y ∈ S

sGxGy=Σ _(x)Σ_(y)(tempX[xSb+x][ySb+y]*tempY[xSb+x][ySb+y]) with x, y ∈ S

sGxdI=Σ _(x)Σ_(y)(−tempX[xSb+x][ySb+y]*temp[xSb+x][ySb+y]) with x, y ∈ S

sGydI=Σ _(x)Σ_(y)(−tempY[xSb+x][ySb+y]*temp[xSb+x][ySb+y]) with x, y ∈ S

-   -    where S is the set of selected positions.        -   i. In one example, the selected positions are x=0, 1, 2, 3            and y=0, 1, 2, 3;        -   ii. In one example, the selected positions are x=0, 2 and            y=0, 1, 2, 3;        -   iii. In one example, the selected positions are x=1, 2 and            y=0, 1, 2, 3;        -   iv. In one example, the selected positions are x=1, 3 and            y=0, 1, 2, 3;        -   v. In one example, the selected positions are x=2, 3 and            y=0, 1, 2, 3;        -   vi. In one example, the selected positions are x=0, 3 and            y=0, 1, 2, 3;        -   vii. In one example, the selected positions are y=0, 2 and            x=0, 1, 2, 3;        -   viii. In one example, the selected positions are y=1, 2 and            x=0, 1, 2, 3;        -   ix. In one example, the selected positions are y=1, 3 and            x=0, 1, 2, 3;        -   x. In one example, the selected positions are y=2, 3 and            x=0, 1, 2, 3;        -   xi. In one example, the selected positions are y=0, 3 and            x=0, 1, 2, 3;        -   xii. In one example, the selected positions are x=−1, 4 and            y=−1, 0, 1, 2, 3, 4;        -   xiii. In one example, the selected positions are x=0, 3 and            y=−1, 0, 1, 2, 3, 4;        -   xiv. In one example, the selected positions are x=1, 2 and            y=−1, 0, 1, 2, 3, 4;        -   xv. In one example, the selected positions are x=−1, 1, 3            and y=−1, 0, 1, 2, 3, 4;        -   xvi. In one example, the selected positions are x=0, 2, 4            and y=−1, 0, 1, 2, 3, 4;        -   xvii. In one example, the selected positions are x=−1, 1, 2,            4 and y=−1, 0, 1, 2, 3, 4;        -   xviii. In one example, the selected positions are x=0, 1, 2,            3 and y=−1, 0, 1, 2, 3, 4;        -   xix. In one example, the selected positions are y=−1, 4 and            x=−1, 0, 1, 2, 3, 4;        -   xx. In one example, the selected positions are y=0, 3 and            x=−1, 0, 1, 2, 3, 4;        -   xxi. In one example, the selected positions are y=1, 2 and            x=−1, 0, 1, 2, 3, 4;        -   xxii. In one example, the selected positions are y=−1, 1, 3            and x=−1, 0, 1, 2, 3, 4;        -   xxiii. In one example, the selected positions are y=0, 2, 4            and x=−1, 0, 1, 2, 3, 4;        -   xxiv. In one example, the selected positions are y=−1, 1, 2,            4 and x=−1, 0, 1, 2, 3, 4;        -   xxv. In one example, the selected positions are y=0, 1, 2, 3            and x=−1, 0, 1, 2, 3, 4;    -   i. The division operation used to derive v_(x) and v_(y) is        replaced in a more sophisticated way.        -   i. In one example, v_(x)=sGx2>0 ? Clip3(−mvRefineThres,            mvRefineThres, −(sGxdI<<3)>>M): 0. M can be Floor(Log            2(sGx2)) or Ceiling(Log 2(sGx2)), depending on the value of            sGx2. For example, M is Ceiling (Log 2(sGx2)) if 3*sGx2 is            larger than 2^(Floor(Log 2(sGx2))+2) , otherwise, M is            Floor(Log 2(sGx2)). In another example, M is Ceiling (Log            2(sGx2)) if sGx2 is larger than T, otherwise, M is Floor(Log            2(sGx2)). E.g. T=(Floor(Log 2(sGx2))+Ceiling (Log            2(sGx2)))/2. In another example, M is Ceiling (Log 2(sGx2))            if sGx2*sGx2 is larger than 2^(2*Floor(Log 2(sGx2))+1) ,            otherwise, M is Floor(Log 2(sGx2)).            -   1. Alternatively, v_(x)=sGx2>0 ? Clip3(−mvRefineThres,                mvRefineThres, −((sGxdI<<3)+Offset)>>M): 0. Offset is an                integer, such as 1<<(M−1).                -   a. Offset can depend on sGx2.        -   ii. In one example, vy=sGy2>0 ? Clip3(−mvRefineThres,            mvRefineThres,            ((sGydI<<3)−((vx*sGxGym)<<12+vx*sGxGys)>>1)>>M: 0. M can be            Floor(Log 2(sGy2)) or Ceiling(Log 2(sGy2)), depending on the            value of sGy2. For example, M is Ceiling (Log 2(sGy2)) if            3*sGy2 is larger than 2^(Floor(Log 2(sGy2)+2), otherwise, M            is Floor(Log 2(sGy2)). In another example, M is Ceiling (Log            2(sGy2)) if sGy2 is larger than T, otherwise, M is Floor(Log            2(sGy2)). E.g. T=(Floor(Log 2(sGy2))+Ceiling (Log            2(sGy2)))/2. In another example, M is Ceiling (Log 2(sGy2))            if sGy2*sGy2 is larger than 2^(2*Floor(Log 2(sGy2)+1),            otherwise, M is Floor(Log 2(sGy2)).            -   1. Alternatively, v_(y)=sGy2>0 ? Clip3(−mvRefineThres,                mvRefineThres,                (((sGydI<<3)−((vx*sGxGym)<<12+vx*sGxGys)>>1)+Offset)>>M: 0.                Offset is an integer such as 1<<(M−1).                -   a. Offset can depend on sGy2.        -   iii. sGxGym and sGxGys are calculated depending on the sign            of sGxGy. Suppose sign(x)=1 if x>=0 and sign(x)=−1 if x<0,            then sGxGym=sign(sGxGy)*|sGxGy|>>W;            sGxGys=sign(sGxGy)*(|sGxGy| & ((1<<W)−1)).            -   1. W can be a fixed number such as 12. Or it may depend                on the sample bit-depth.        -   iv. The division operation in the BIO procedure is            calculated by a look-up table.            -   (i) The same look-up table is also used in CCLM to                replace the division operation.

Example 11. The decision of BIO on/off based on whole-block andsub-block SAD calculations may be simplified by just calculating thesub-block SAD calculation. Alternatively, the SAD calculation may bereplaced by other rules, such as MR-SAD.

Example 12. The SAD calculation in BIO is only done with samples onselected positions.

-   -   a. subCuLevelSumAbsoluteDiff[xSbIdx][ySbIdx]=Σ_(i)Σ_(j)        Abs(predSamplesL0L[(xSbIdx<<2)+1+i][(ySbIdx<<2)+1+j]−predSamplesL1L[(xSbIdx<<2)+1+i][(ySbIdx<<2)+1+j])        with i, j∈S where S is the set of selected positions.        -   i. In one example, the selected positions are i=0, 2 and            j=0, 1, 2, 3;        -   ii. In one example, the selected positions are i=1, 2 and            j=0, 1, 2, 3;        -   iii. In one example, the selected positions are i=1, 3 and            j=0, 1, 2, 3;        -   iv. In one example, the selected positions are i=2, 3 and            j=0, 1, 2, 3;        -   v. In one example, the selected positions are i=0, 3 and            j=0, 1, 2, 3;        -   vi. In one example, the selected positions are j=0, 2 and            i=0, 1, 2, 3;        -   vii. In one example, the selected positions are j=1, 2 and            i=0, 1, 2, 3;        -   viii. In one example, the selected positions are j=1, 3 and            i=0, 1, 2, 3;        -   ix. In one example, the selected positions are j=2, 3 and            i=0, 1, 2, 3;        -   x. In one example, the selected positions are j=0, 3 and            i=0, 1, 2, 3;        -   xi. In one example, the selected positions are i=0, 3 and            j=0, 3;        -   xii. In one example, the selected positions are i=1, 2 and            j=1, 2;    -   b. Alternatively,        subCuLevelSumAbsoluteDiff[xSbIdx][ySbIdx]=max_(i,j)        Abs(predSamplesL0L[(xSbIdx<<2)+1+i][(ySbIdx<<2)+1+j]-predSamplesL1L[(xSbIdx<<2)+1+i][(ySbIdx<<2)+1+j])        with i, j∈S where S is the set of selected positions.    -   c. Alternatively,        subCuLevelSumAbsoluteDiff[xSbIdx][ySbIdx]=min_(i,j)        Abs(predSamplesL0L[(xSbIdx<<2)+1+i][(ySbIdx<<2)+1+j]-predSamplesL1L[(xSbIdx<<2)+1+i][(ySbIdx<<2)+1+j])        with i, j∈S where S is the set of selected positions.    -   d. The threshold value subCuLevelAbsDiffThres may be adaptive.        -   i. It may depend on coding information such as QP and POC            distance        -   ii. It may be signaled from the encoder to the decoder in            VPS/SPS/PPS/slice header/tile group header/tile/CTU/CU.

Example 13. The SAD calculation in BIO is only done with samples onselected sub-blocks.

-   -   a. Alternatively, furthermore, the SAD calculation for each        sub-block may only involve partial of samples within one        sub-block.    -   b. cuLevelSumAbsoluteDiff=Σ_(xsbIdx) Σ_(ySbIdx)        subCuLevelSumAbsoluteDiff[xSbIdx][ySbIdx] with xSbIdx, ySbIdx∈S    -    where S is the set of selected sub-blocks.        -   i. In one example, the selected positions are xSbIdx % 2==0;        -   ii. In one example, the selected positions are xSbIdx %            2==1;        -   iii. In one example, the selected positions are xSbIdx %            4==0;        -   iv. In one example, the selected positions are xSbIdx==0 or            xSbIdx==(cbWidth>>2)−1;        -   v. In one example, the selected positions are xSbIdy % 2==0;        -   vi. In one example, the selected positions are xSbIdy %            2==1;        -   vii. In one example, the selected positions are xSbIdy %            4==0;        -   viii. In one example, the selected positions are xSbIdy==0            or xSbIdy==(cbHeight>>2)−1;        -   ix. In one example, the selected positions are (xSbIdy==0 or            xSbIdy==(cbHeight>>2)−1), and (xSbIdy==0 or            xSbIdy==(cbHeight>>2)−1);    -   c. cuLevelSumAbsoluteDiff=Max_(xSbIdx, ySbIdx)        subCuLevelSumAbsoluteDiff[xSbIdx][ySbIdx] with xSbIdx, ySbIdx∈S    -   d. cuLevelSumAbsoluteDiff=Min_(xSbIdx, ySbIdx)        subCuLevelSumAbsoluteDiff[xSbIdx][ySbIdx] with xSbIdx, ySbIdx∈S    -   e. The threshold value cuLevelAbsDiffThres may be adaptive.        -   i. It may depend on coding information such as QP and POC            distance        -   ii. It may depend on the coding mode like AMVP mode, merge            mode or MMVD (merge with MV difference) mode.        -   iii. It may be signaled from the encoder to the decoder in            VPS/SPS/PPS/slice header/tile group header/tile/CTU/CU.

Example 14. For Examples 12 and 13, the proposed methods may also beapplicable to other cases when SAD is replaced by other rules. That is,only partial of samples within one sub-block, and/or partial ofsub-blocks may be considered to decide the usage of BIO.

Example 15. How to derive the variables in BIO procedure may bedifferent when GBi is applied or not.

-   -   a. Alternatively, furthermore, the derivation may be different        for GBi with different weighting values.    -   b. Suppose the weighting values are W0 and W1 for Ref0 and Ref1        in the GBi process, then prediction blocks are firstly weighted        before deriving the variables used in BIO procedure, such as SAD        calculation, gradient calculation.        -   i. Denoted the two prediction blocks by predSamplesL0[x][y]            and predSamplesL1[x][y]. W0*predSamplesL0[x][y] and            predSamplesL1[x][y] and W1*predSamplesL1[x][y] are used as            the inputs for BIO.    -   c. Alternatively, predSamplesL0[x][y] is pre-calculated as        Shift(W0*predSamplesL0[x][y], S0) and predSamplesL1[x][y] is        pre-calculated as Shift(W1*predSamplesL1[x][y], S1) before        deriving the variables used in BIO procedure.        -   i. S0 and S1 may depend on the sample bit-depth.        -   ii. S0 and S1 may depend on W0 and W1.        -   iii. S0 and S1 may be fixed numbers such as 2.    -   d. Alternatively, the values of the correction item may also be        weighted when generating the final prediction block of one        block.        -   i. When deriving parameters used in the BIO procedure, the            two prediction blocks without being weighted are used as            inputs to BIO as prior art. However, the final prediction            blocks are generated by weighted values of prediction blocks            and weighted values of correction item.        -   ii. The weights applied to correction item may be the same            as that used for prediction blocks. Alternatively, different            weights may be applied to correction item.

Example 16. It is proposed the correction item value should be in aspecific range.

-   -   (a) For example, correction item is clipped to [minCorrection,        maxCorrection].        -   i. minCorrection and maxCorrection may be fixed, e.g., −32            and 32;        -   ii. minCorrection and maxCorrection may depend on the sample            bit-depth. For example, minCorrection=−(32<<(BitDepth−8)),            maxCorrection=32<<(BitDepth−8)).        -   iii. minCorrection and maxCorrection may depend on block            width and/or height.        -   iv. minCorrection and maxCorrection may be signaled from the            encoder to the decoder in VPS/SPS/PPS/slice header/tile            group header/tile/CTU line/CTU/CU.    -   (b) For example, correction item is divided by a factor or right        shifted if it is out of the range.

Example 17. It is proposed that the final prediction output in BIOshould be in a specific range.

-   -   (a) For example, the final prediction output in BIO is clipped        to [minPred, maxPred].        -   i. minPred and maxPred may be fixed numbers such as −32 and            32;        -   ii. minPred and maxPred may depend on the sample bit-depth.            For example, minPred=−(32<<(BitDepth−8)),            maxCorrection=32<<(BitDepth−8)).        -   iii. minPred and maxPred may depend on block width and/or            height.        -   iv. minPred and maxPred may be signaled from the encoder to            the decoder in VPS/SPS/PPS/slice header/tile group            header/tile/CTU line/CTU/CU.

The examples described above may be incorporated in the context of themethod described below, e.g., methods 2810, 2820, 2830, 2840, 2850 and2860, which may be implemented at a video decoder or a video encoder.

FIG. 28A shows a flowchart of an exemplary method for video processing.The method 2810 includes, at step 2812, determining to use, for aconversion between a current block of a video and a bitstreamrepresentation of the video, a first linear optimization model for theconversion using a first coding mode, the first linear optimizationmodel being derived from a second linear optimization model that is usedfor the conversion using a second coding mode.

The method 2810 includes, at step 2814, performing, based on thedetermining, the conversion.

FIG. 28B shows a flowchart of an exemplary method for video processing.The method 2820 includes, at step 2822, enabling, based on one or morepicture order count (POC) parameters associated with a picture of acurrent block of video, either a first prediction mode or a secondprediction mode different from the first prediction mode, the firstprediction mode being a coding mode using optical flow.

The method 2820 includes, at step 2824, performing, based on the firstmode or the second mode, a conversion between the current block and abitstream representation of the video.

FIG. 28C shows a flowchart of an exemplary method for video processing.The method 2830 includes, at step 2832, consecutively deriving, based oncoded information associated with a current block of video, one or morevelocity vectors (v_(x), v_(y)) associated with a reference picture ofthe current block.

The method 2830 includes, at step 2834, performing, based on the one ormore velocity vectors, a conversion between the current block and abitstream representation of the video, the coded information comprisinga value of a horizontal component of a motion vector of the currentblock, a value of a vertical component of the motion vector of thecurrent block, or a size of the current block.

FIG. 28D shows a flowchart of an exemplary method for video processing.The method 2840 includes, at step 2842, performing, upon a determinationthat a coding mode using optical flow has been enabled for a currentblock of video, a filtering operation using a single type ofinterpolation filter for each color component of the current block.

The method 2840 includes, at step 2844, performing, based on thefiltering operation, a conversion between the current block and abitstream representation of the video.

FIG. 28E shows a flowchart of an exemplary method for video processing.The method 2850 includes, at step 2852, determining to use, for aconversion between a current block of a video and a bitstreamrepresentation of the video, a gradient value computation algorithm foran optical flow tool.

The method 2850 includes, at step 2854, performing, based on thedetermining, the conversion.

FIG. 28F shows a flowchart of an exemplary method for video processing.The method 2860 includes, at step 2862, making a decision, based on oneor more sum of absolute difference (SAD) calculations for a sub-block ofa current block of video, regarding a selective enablement of a codingmode using optical flow for the current block.

The method 2860 includes, at step 2864, performing, based on thedecision, a conversion between the current block and a bitstreamrepresentation of the current block.

FIG. 28G shows a flowchart of an exemplary method for video processing.The method 2870 includes, at step 2872, deriving, based on a selectiveenablement of a generalized bi-prediction improvement (GBi) process fora current block of video, one or more parameters of a coding mode usingoptical flow for the current block.

The method 2870 includes, at step 2874, performing, based on the one ormore parameters of the coding mode using optical flow, a conversionbetween the current block and a bitstream representation of the video.

FIG. 28H shows a flowchart of an exemplary method for video processing.The method 2880 includes, at step 2882, performing, for a current blockof video coded with a coding mode using optical flow, a clippingoperation on a correction term of a final prediction output of thecoding mode using optical flow.

The method 2880 includes, at step 2884, performing, based on the finalprediction output, a conversion between the current block and abitstream representation of the video.

FIG. 28I shows a flowchart of an exemplary method for video processing.The method 2890 includes, at step 2892, performing, upon a determinationthat a coding mode using optical flow has been enabled for a currentblock of video, a filtering operation using a single type ofinterpolation filter for each color component of the current block.

The method 2890 includes, at step 2894, performing, upon a determinationthat at least one sample of the current block is located outside apredetermined range, a padding operation.

The method 2890 includes, at step 2896, performing, based on thefiltering operation and the padding operation, a conversion between thecurrent block and a bitstream representation of the video.

In some embodiments, the following technical solutions may beimplemented:

A1. A method of video processing, comprising: determining to use, for aconversion between a current block of a video and a bitstreamrepresentation of the video, a first linear optimization model for theconversion using a first coding mode, wherein the first linearoptimization model is derived from a second linear optimization modelthat is used for the conversion using a second coding mode; andperforming, based on the determining, the conversion.A2. The method of solution A1, wherein the second coding mode is abi-directional optical flow (BDOF) prediction mode.A3. The method of solution A2, wherein the second linear optimizationmodel comprises N groups of samples (u_(k), v_(k), w_(k)) as an inputand two output parameters, a and b, and wherein the second linearoptimization model minimizes or approximately minimizes

$\sum\limits_{k = 0}^{N - 1}{\left( {u_{k} + {a \times v_{k}} + {b \times w_{k}}} \right)^{2}.}$

A4. The method of solution A3, wherein u_(k)=(I⁽⁰⁾(x,y)−I⁽¹⁾(x,y)v_(k)=(G_(x) ⁽⁰⁾(x,y)−G_(x) ⁽¹⁾(x,y)), w_(k)=(G_(y) ⁽⁰⁾(x,y)−G_(y)⁽¹⁾(x,y)), a=v_(x), and b=v_(y), wherein I^((i))(x,y) represents a lumasample at pixel location (x,y) in reference frame i, and wherein G_(x)^((i))(x,y) and G_(y) ^((i))(x,y) represent a horizontal gradient and avertical gradient of the luma sample, respectively.A5. The method of solution A3, wherein u_(k)=−C(n), v_(k)=L(n), w_(k)=1,a=α, and b=β, wherein C(n) represents a top and a left neighboringreconstructed chroma sample, wherein L (n) represents a top and a leftneighboring reconstructed luma sample, and wherein α and β representparameters of the second linear optimization model.A6. The method of solution A1, wherein the second coding mode is across-component linear model prediction mode.A7. A method of video processing, comprising: enabling, based on one ormore picture order count (POC) parameters associated with a picture of acurrent block of video, either a first prediction mode or a secondprediction mode different from the first prediction mode, wherein thefirst prediction mode is a coding mode using optical flow; andperforming, based on the first mode or the second mode, a conversionbetween the current block and a bitstream representation of the video.A8. The method of solution A7, further comprising: refraining fromenabling the coding mode using optical flow, wherein a first referencepicture (R₀) and a second reference picture (R₁) are associated with acurrent picture comprising the current block, wherein τ₀ is a differencebetween a picture order count of the current picture and a picture ordercount of the first reference picture (τ₀=POC(current)−POC(R₀)), andwherein τ₁ is a difference between a picture order count of the secondreference picture and the picture order count of the current picture(τ₁=POC(R₁)−POC(current)).A9. The method of solution A8, wherein abs(τ₀)≥T₀ or abs(τ₁)≥T₁, whereinT₀ and T₁ are integers.A10. The method of solution A8, wherein abs(τ₀)≥T₀ and abs(τ₁)≥T₁,wherein T₀ and T₁ are integers.A11. The method of solution A9 or A10, wherein T₀=T_(i)=4.A12. The method of solution A8, wherein abs(τ₀)+abs(τ₁)≥T₀, wherein T₀is an integer.A13. The method of solution A12, wherein T₀=8.A14. The method of solution A8, wherein abs(abs(τ₀)−abs(τ₁))≥T₀, whereinT₀ is an integer.A15. The method of solution A14, wherein T₀=0.A16. The method of solution A8, abs(τ₀)≥abs(τ₁)×T₀ andabs(τ₁)≥abs(τ₀)×T₀, wherein T₀ is an integer.A17. The method of solution A16, wherein T₀=8.A18. The method of any of solutions A9 to A17, wherein T₀ is signaled ina video parameter set (VPS), a sequence parameter set (SPS), a pictureparameter set (PPS), a slice header, a tile group header, a tile, acoding unit (CU) or a coding tree unit (CTU).A19. The method of solution A7, wherein the coding mode using opticalflow is enabled, wherein one or more velocity vectors (v_(x), v_(y)) fora current picture comprising the current block are based on the one ormore POC distances, and wherein a first reference picture (R₀) and asecond reference picture (R₁) are associated with the current picture.A20. The method of solution A19, wherein τ₀ is a difference between apicture order count of the current picture and a picture order count ofthe first reference picture (T₀=POC(current)−POC(R₀)), wherein τ₁ is adifference between a picture order count of the second reference pictureand the picture order count of the current picture(τ₁=POC(R₁)−POC(current)), wherein

${v_{x}^{(1)} = {v_{x}\frac{2{\tau_{0}}}{{\tau_{0}} + {\tau_{1}}}}},{v_{x}^{(0)} = {{- v_{x}}\frac{2{\tau_{1}}}{{\tau_{0}} + {\tau_{1}}}}},{v_{y}^{(1)} = {v_{y}\frac{2{\tau_{0}}}{{\tau_{0}} + {\tau_{1}}}}},{v_{y}^{(0)} = {{- v_{y}}\frac{2{\tau_{1}}}{{\tau_{0}} + {\tau_{1}}}}},$

wherein (v_(x) ⁽⁰⁾, v_(y) ⁽⁰⁾) are velocity vectors for the firstreference picture and (v_(x) ⁽¹⁾, v_(y) ⁽¹⁾) are velocity vectors forthe second reference picture.A21. The method of solution A19, wherein the one or more velocityvectors are derived in an iterative manner.A22. The method of solution A19, wherein the one or more velocityvectors are based on a third prediction mode, and wherein the thirdprediction mode is DMVR or bilateral matching.A23. The method of solution A22, wherein MV0 and MV1 are motion vectorsfor the first and second reference pictures, respectively, and whereinMV0′ and MV1′ are initial motion vectors for the third prediction mode.A24. The method of solution A23, wherein MV0′=MV0+(v_(x), v_(y)) andMV1′=MV1−(v_(x), v_(y)).A25. The method of solution A23, wherein MV0′=MV0−(v_(x), v_(y)) andMV1′=MV1+(v_(x), v_(y)).A26. The method of solution A7, wherein the coding mode using opticalflow is enabled and applied to a subset of samples of the current block,wherein the subset of samples excludes all samples of the current block.A27. The method of solution A26, wherein the subset of samples excludessamples in a first row, last row, first column or last column of thecurrent block.A28. The method of solution A7, wherein the coding mode using opticalflow is enabled and applied to a subset of samples of a sub-block of thecurrent block, wherein the subset of samples is not equivalent to allsamples of the sub-block of the current block.A29. The method of solution A28, wherein the subset of samples excludessamples in a first row, last row, first column or last column of thesub-block of the current block.A30. The method of any of solutions A1 to A29, wherein the coding modeusing optical flow is a bi-directional optical flow (BDOF) predictionmode.A31. A method of video processing, comprising: consecutively deriving,based on coded information associated with a current block of video, oneor more velocity vectors (v_(x), v_(y)) associated with a referencepicture of the current block; and performing, based on the one or morevelocity vectors, a conversion between the current block and a bitstreamrepresentation of the video, wherein the coded information comprises avalue of a horizontal component of a motion vector of the current block,a value of a vertical component of the motion vector of the currentblock, or a size of the current block.A32. The method of A31, wherein v_(y) is derived first, and whereinv_(x) is derived based on a value of v_(y).A33. The method of solution A31, further comprising: refining, based onthe one or more velocity vectors, at least one prediction or at leastone gradient of the reference picture; and deriving, subsequent to therefining, the one or more velocity vectors based on the referencepicture.A34. The method of solution A33, wherein the refining and deriving areperformed multiple times until a difference between consecutive valuesof one of the one or more velocity vectors is less than a predeterminedthreshold.A35. The method of any of solutions A1 to A34, wherein the conversiongenerates the current block from the bitstream representation.A36. The method of any of solutions A1 to A34, wherein the conversiongenerates the bitstream representation from the current block.A37. An apparatus in a video system comprising a processor and anon-transitory memory with instructions thereon, wherein theinstructions upon execution by the processor, cause the processor toimplement the method in any one of solutions A1 to A36.A38. A computer program product stored on a non-transitory computerreadable media, the computer program product including program code forcarrying out the method in any one of solutions A1 to A36.

In some embodiments, the following technical solutions may beimplemented:

B1. A method of video processing, comprising: performing, upon adetermination that a coding mode using optical flow has been enabled fora current block of video, a filtering operation using a single type ofinterpolation filter for each color component of the current block; andperforming, based on the filtering operation, a conversion between thecurrent block and a bitstream representation of the video.B2. The method of solution B1, wherein the color component comprises aluma component and the single type of interpolation filter comprises an8-tap interpolation filter.B3. The method of solution B1, wherein a first number of samples of thecurrent block used in the filtering operation is fewer than a secondnumber of samples required by the single type of interpolation filter,thereby reducing a memory bandwidth of the filtering operation.B4. The method of solution B3, wherein the second number of samples isequal to a number of samples of the current block used in the filteringoperation when the coding mode using optical flow is not applied.B5. The method of solution B1, wherein a number of samples of thecurrent block are used in the filtering operation, and wherein themethod further comprises: performing a padding operation upon adetermination that the coding mode using optical flow is applied.B6. The method of solution B1, wherein a size of the current block isM×N, wherein a first number of samples required by a gradientcalculation is (M+G)×(N+G), wherein the single type of interpolationfilter comprises L taps, wherein a second number of samples required bythe filtering operation is (M+G+L−1)×(N+G+L−1), wherein a third numberof samples used in the filtering operation is (M+L−1+k)×(N+L−1+k),wherein M, N, G, and L are positive integers, wherein k is an integerless than G, and wherein a fourth number of samples that comprise adifference between the second number of samples and the third number ofsamples are padded.B7. The method of solution B6, wherein M=N=8, L=8 and G=2.B8. The method of solution B6 or B7, wherein k=0 or k=1.B9. The method of solution B1, wherein a coordinate of a top-left pixelof the current block is (0,0), and wherein the method further comprises:refraining from calculating a gradient value and applying the codingmode using optical flow to a pixel in a predetermined position.B10. The method of solution B9, wherein an x-coordinate of thepredetermined position is −1 or W, wherein W is a width of the currentblock, and wherein W is a positive integer.B11. The method of solution B9, wherein an y-coordinate of thepredetermined position is −1 or H, wherein H is a height of the currentblock, and wherein H is a positive integer.B12. The method of solution B1, wherein a coordinate of a top-left pixelof the current block is (0,0), and wherein the method further comprises:modifying a calculation of a gradient value for a pixel in apredetermined position.B13. The method of solution B12, wherein an x-coordinate of thepredetermined position is −1 or W, wherein W is a width of the currentblock, and wherein W is a positive integer.B14. The method of solution B12, wherein an y-coordinate of thepredetermined position is −1 or H, wherein H is a height of the currentblock, and wherein H is a positive integer.B15. The method of solution B13 or B14, wherein the calculation of thegradient value comprises:

${{gradientHL}\;{{0\lbrack x\rbrack}\lbrack y\rbrack}} = \left\{ {\begin{matrix}{\left( {{predSa{mpleL}\;{{0\ \left\lbrack {{hx} + 1} \right\rbrack}\lbrack{vy}\rbrack}} - {{predSampleL}\;{{0\lbrack{hx}\rbrack}\ \left\lbrack {vy} \right\rbrack}}} \right)\ \operatorname{>>}4} & {{{if}\mspace{14mu}{hx}} = 1} \\{\left( {{pr{edSampleL}\;{{0\ \left\lbrack {hx} \right\rbrack}\lbrack{vy}\rbrack}} - {{predSampleL}\;{{0\ \left\lbrack {{hx} - 1} \right\rbrack}\ \left\lbrack {vy} \right\rbrack}}} \right)\ \operatorname{>>}4} & {{{if}\mspace{14mu}{hx}} = W} \\{\left( {{p{{{redSampleL0}\left\lbrack {{hx} + 1} \right\rbrack}\lbrack{vy}\rbrack}} - {{{predSampleL0}\left\lbrack {{hx} - 1} \right\rbrack}\left\lbrack {vy} \right\rbrack}} \right)\operatorname{>>}4} & {Otherwise}\end{matrix},} \right.$

wherein W is a width of the current block, wherein predSampleL0 is anarray comprising luma prediction samples for the current block fromreference list L0, wherein gradientHL0 is a horizontal component of thegradient value derived from the reference list L0, and wherein hx andv_(y) are integer coordinate indexes.

B16. The method of solution B13 or B14, wherein the calculation of thegradient value comprises:

${{gradientVL}\;{{0\lbrack x\rbrack}\lbrack y\rbrack}} = \left\{ {\begin{matrix}{\left( {{predSa{mpleL}\;{{0\ \lbrack{hx}\rbrack}\left\lbrack {{vy} + 1} \right\rbrack}} - {{predSampleL}\;{{0\lbrack{hx}\rbrack}\ \left\lbrack {vy} \right\rbrack}}} \right)\ \operatorname{>>}3} & {{{if}\mspace{14mu}{vy}} = 1} \\{\left( {{pr{edSampleL}\;{{0\ \left\lbrack {hx} \right\rbrack}\lbrack{vy}\rbrack}} - {{predSampleL}\;{0\ \left\lbrack {{hx}\ \left\lbrack {{vy} - 1} \right\rbrack} \right\rbrack}}} \right)\ \operatorname{>>}3} & {{{if}\mspace{14mu}{vy}} = H} \\{\left( {{p{{{redSampleL0}\lbrack{hx}\rbrack}\left\lbrack {{vy} + 1} \right\rbrack}} - {{{predSampleL0}\lbrack{hx}\rbrack}\left\lbrack {{vy} - 1} \right\rbrack}} \right)\operatorname{>>}4} & {Otherwise}\end{matrix},} \right.$

wherein W is a width of the current block, wherein predSampleL0 is anarray comprising luma prediction samples from the current block, whereingradientVL0 is a vertical component of the gradient value derived fromreference list L0, and wherein hx and vy are integer coordinate indexes.

B17. The method of solution B1, further comprising: padding, prior tothe calculation of the gradient value, one or more outer samples of thecurrent block instead of interpolating the one or more outer samples.B18. The method of solution B1, wherein one or more gradientcalculations used in the coding mode with optical flow are identical tothose used in an adaptive loop filter (ALF).B19. A method of video processing, comprising: performing, upon adetermination that a coding mode using optical flow has been enabled fora current block of video, a filtering operation using a single type ofinterpolation filter for each color component of the current block;performing, upon a determination that at least one sample of the currentblock is located outside a predetermined range, a padding operation; andperforming, based on the filtering operation and the padding operation,a conversion between the current block and a bitstream representation ofthe video.B20. The method of solution B19, wherein the padding operation isperformed prior to performing a gradient calculation.B21. The method of solution B19, wherein the predetermined range isbased on a height or a width of the current block.B22. The method of any of solutions B1 to B21, wherein the coding modeusing optical flow comprises a bi-directional optical flow (BDOF)prediction mode.B23. The method of any of solutions B1 to B22, wherein the conversiongenerates the current block from the bitstream representation.B24. The method of any of solutions B1 to B22, wherein the conversiongenerates the bitstream representation from the current block.B25. An apparatus in a video system comprising a processor and anon-transitory memory with instructions thereon, wherein theinstructions upon execution by the processor, cause the processor toimplement the method in any one of solutions B1 to B24.B26. A computer program product stored on a non-transitory computerreadable media, the computer program product including program code forcarrying out the method in any one of solutions B1 to B24.

In some embodiments, the following technical solutions may beimplemented:

C1. A method of video processing. comprising: determining to use, for aconversion between a current block of a video and a bitstreamrepresentation of the video, a gradient value computation algorithm foran optical flow tool; and performing, based on the determining, theconversion, wherein the gradient value computation algorithm differsfrom a legacy gradient value computation algorithm that comprises:

gradientHL0[x][y]=(predSampleL0[hx+1][vy]−predSampleL0[hx−1][vy])>>4,

gradientVL0[x][y]=(predSampleL0[hx][vy+1]−predSampleL0[hx][vy−1])>>4,

gradientHL1[x][y]=(predSampleL1[hx+1][vy]−predSampleL1[hx−1][vy])>>4,

and

gradientVL1[x][y]=(predSampleL1[hx][vy+1]−predSampleL1[hx][vy−1])>>4,

wherein gradientHL0 is a horizontal component of the gradient valuederived from reference list L0, wherein gradientVL0 is a verticalcomponent of the gradient value derived from reference list L0, whereingradientHL1 is a horizontal component of the gradient value derived fromreference list L1, wherein gradientVL1 is a vertical component of thegradient value derived from reference list L1, wherein predSampleL0 isan array comprising luma prediction samples for the current block fromreference list L0, wherein predSampleL1 is an array comprising lumaprediction samples for the current block from reference list L1, andwherein hx and vy are integer coordinate indexes.C2. The method of solution C1, wherein modifying the gradient valuecomputation comprises shifting a gradient value by a predeterminedpositive integer (S), and wherein S≠4.C3. The method of solution C2, wherein S=6.C4. The method of solution C2, wherein S=B−P, wherein B is a bit-depthof a sample of the current block, and wherein P is a positive integer.C5. The method of solution C4, wherein P=6, and wherein B=8 or 12.C6. The method of solution C1, wherein modifying the gradient valuecomputation comprises clipping a gradient value such that the gradientvalue is representable as a K-bit integer, and wherein K is a positiveinteger.C7. The method of solution C6, wherein K=8 or 16.C8. The method of solution C1, wherein the gradient value computationalgorithm for the optical flow tool comprises a computation of a firstinternal variable (temp), a second interval variable (tempX) and a thirdinternal variable (tempY), and wherein the computation is defined as:

temp[x][y]=SignShift(predSampleL0[hx][vy]−predSampleL1[hx][vy],S1),

tempX[x][y]=SignShift(gradientHL0[x][y]+gradientHL1[x][y],S2),

and

tempY[x][y]=SignShift(gradientVL0[x][y]+gradientVL1[x][y],S3),

wherein S1, S2 and S3 are integers, and wherein SignShift(x, s) isdefined as:

${{SignShift}\left( {x,s} \right)} = \left\{ {\begin{matrix}{{\left( {x + {off}} \right)\operatorname{>>}s}\;} & {x \geq 0} \\{- \left( {\left( {{- x} + {off}} \right)\operatorname{>>}s} \right)} & {x < 0}\end{matrix},{and}} \right.$

wherein off is an integer.

C9. The method of solution C8, wherein S1=6 and S2=S3=3.C10. The method of solution C8, wherein S1, S2 and S3 are based on abit-depth of a sample of the current block (B).C11. The method of solution C10, wherein S1=B−P1, S2=B−P2 and S3=B−P3,wherein P1, P2 and P3 are integers.C12. The method of solution C11, wherein B=8, 10 or 12, and wherein P1=4and P2=P3=7.C13. The method of solution C1, wherein the gradient value computationalgorithm for the optical flow tool comprises a computation of a firstinternal variable (temp), a second interval variable (tempX) and a thirdinternal variable (tempY) are representable by a K1-bit integer, aK2-bit integer and a K3-bit integer, respectively, and wherein K1, K2and K3 are positive integers.C14. The method of solution C13, wherein K1=8 or 16, K2=8 or 16 and K3=8or 16.C15. The method of solution C13 or C14, wherein the computation of temp,tempX and tempY is followed by a clipping operation defined as:

temp[x][y]=Clip3(−2^(K1−1), 2^(K1−1)−1, gradientHL0[x][y]),

tempX[x][y]=Clip3(−2^(K2−1), 2^(K2−1)−1, gradientVL0[x][y]),

and

tempY[x][y]=Clip3(−2^(K3−1), 2^(K3−1)−1, gradientHL1[x][y]),

wherein Clip3(x, min, max) is defined as:

${{Clip}\; 3\left( {x,\min,\max} \right)} = \left\{ {\begin{matrix}\min & {x < \min} \\x & {\min \leq x \leq \max} \\{\max\ } & {x > \max}\end{matrix}.} \right.$

C16. The method of solution C1, wherein the gradient value computationalgorithm for the optical flow tool comprises a computation of aplurality of internal variables comprising sGx2, sGy2, sGxGy, sGxdI andsGydI that are representable by a K1-bit integer, a K2-bit integer, aK3-bit integer, a K4-bit integer and a K5-bit integer, respectively, andwherein K1, K2, K3, K4 and K5 are positive integers.C17. The method of solution C16, wherein K1=8 or 16, K2=8 or 16, K3=8 or16, K4=8 or 16 and K5=8 or 16.C18. The method of solution C16 and C17, wherein the computation ofsGx2, sGy2, sGxGy, sGxdI and sGydI is followed by a shifting operationdefined as:

sGx2=Shift(sGx2, S1),

sGy2=Shift(sGy2, S2),

sGxGy=SignShift(sGxGy, S3),

sGxdI=SignShift(sGxdI, S4),

and

sGydI=SignShift(sGydI, S5),

wherein S1, S2, S3, S4 and S5 are positive integers, wherein Shift(x,s)=(x+off)>>s, and wherein SignShift(x, s) is defined as:

${{SignShift}\left( {x,s} \right)} = \left\{ {\begin{matrix}{{\left( {x + {off}} \right)\operatorname{>>}s}\;} & {x \geq 0} \\{- \left( {\left( {{- x} + {off}} \right)\operatorname{>>}s} \right)} & {x < 0}\end{matrix},{and}} \right.$

wherein off is an integer.C19. The method of solution C18, wherein S1, S2, S3, S4 and S5 are equalto 4 or 5.C20. The method of solution C18, wherein S1, S2, S3, S4 and S5 are basedon a bit-depth of a sample of the current block (B).C21. The method of solution C8, wherein S1=B−P1, S2=B−P2, S3=B−P3,S4=B−P4 and S5=B−P5, wherein P1, P2, P3, P4 and P5 are integers.C22. The method of solution C21, wherein B=8, 10 or 12.C23. The method of solution C16 and C17, wherein the computation ofsGx2, sGy2, sGxGy, sGxdI and sGydI is followed by a clipping operationdefined as:

sGx2=Clip3(0, 2^(K1−)1, sGx2),

sGy2=Clip3(0, 2^(K2−)1, sGy2),

sGxGy=Clip3(−2^(K3−1), 2^(K3−1)−1, sGxGy),

sGxdI=Clip3(−2^(K4−1), 2^(K4−1)−sGxdi),

and

sGydI=Clip3(−2^(K5−1), 2^(K5−1)−1, sGydI)

wherein Clip3(x, min, max) is defined as:

${{Clip}\; 3\left( {x,\min,\max} \right)} = \left\{ {\begin{matrix}\min & {\ {x < \min}} \\x & {\min \leq x \leq \max} \\{\max\ } & {x > \max}\end{matrix}.} \right.$

C24. The method of any of solutions C1 to C23, wherein the optical flowtool comprises a bi-directional optical flow (BDOF) tool.C25. A method of video processing, comprising: making a decision, basedon one or more sum of absolute difference (SAD) calculations for asub-block of a current block of video, regarding a selective enablementof a coding mode using optical flow for the current block; andperforming, based on the decision, a conversion between the currentblock and a bitstream representation of the current block.C26. The method of solution C25, wherein the SAD calculations comprise amean-removed sum of absolute difference (MR-SAD) calculation.C27. The method of solution C25 or C26, wherein the SAD calculations areperformed on samples in predetermined locations in the current block.C28. The method of solution C25 or C26, wherein the SAD calculations areperformed on samples in predetermined locations in the sub-block of thecurrent block.C29. A method of video processing, comprising: deriving, based on aselective enablement of a generalized bi-prediction improvement (GBi)process for a current block of video, one or more parameters of a codingmode using optical flow for the current block; and performing, based onthe one or more parameters of the BDOF prediction mode, a conversionbetween the current block and a bitstream representation of the video.C30. A method of video processing, comprising: performing, for a currentblock of video coded with a coding mode using optical flow, a clippingoperation on a correction term of a final prediction output of thecoding mode using optical flow; and performing, based on the finalprediction output, a conversion between the current block and abitstream representation of the video.C31. The method of solution C30, wherein the correction term is clippedto a range [minCorrection, maxCorrection], wherein minCorrection andmaxCorrection are integers.C32. The method of solution C31, wherein minCorrection=−32 andmaxCorrection=32.C33. The method of solution C31, wherein minCorrection and maxCorrectionare based on a sample bit-depth.C34. The method of solution C31, wherein minCorrection and maxCorrectionare based on a height or a width of the current video block.C35. The method of solution C31, wherein minCorrection and maxCorrectionare signaled in a video parameter set (VPS), a sequence parameter set(SPS), a picture parameter set (PPS), a slice header, a tile groupheader, a tile, a coding unit (CU) or a coding tree unit (CTU).C36. The method of solution C30, wherein the final prediction output isclipped to a range [minPred, maxPred], wherein minPred and maxPred areintegers.C37. The method of solution C36, wherein minPred=−32 and maxPred=32.C38. The method of solution C36, wherein midPred and maxPred are basedon a sample bit-depth.C39. The method of solution C36, wherein midPred and maxPred are basedon a height or a width of the current video block.C40. The method of solution C36, wherein midPred and maxPred aresignaled in a video parameter set (VPS), a sequence parameter set (SPS),a picture parameter set (PPS), a slice header, a tile group header, atile, a coding unit (CU) or a coding tree unit (CTU).C41. The method of any of solutions C30 to C40, wherein the correctionterm comprises a derived prediction offset for a sample based on thecoding mode using optical flow.C42. The method of any of solutions C25 to C41, wherein the coding modeusing optical flow comprises a bi-directional optical flow (BDOF)prediction mode.C43. The method of any of solutions C1 to C42, wherein the conversiongenerates the current block from the bitstream representation.C44. The method of any of solutions C1 to C42, wherein the conversiongenerates the bitstream representation from the current block.C45. An apparatus in a video system comprising a processor and anon-transitory memory with instructions thereon, wherein theinstructions upon execution by the processor, cause the processor toimplement the method in any one of solutions C1 to C44.C46. A computer program product stored on a non-transitory computerreadable media, the computer program product including program code forcarrying out the method in any one of solutions C1 to C44.

6. Example Implementations of the Disclosed Technology

FIG. 29 is a block diagram of a video processing apparatus 2900. Theapparatus 2900 may be used to implement one or more of the methodsdescribed herein. The apparatus 2900 may be embodied in a smartphone,tablet, computer, Internet of Things (IoT) receiver, and so on. Theapparatus 2900 may include one or more processors 2902, one or morememories 2904 and video processing hardware 2906. The processor(s) 2902may be configured to implement one or more methods (including, but notlimited to, method 2800) described in the present document. The memory(memories) 2904 may be used for storing data and code used forimplementing the methods and techniques described herein, although someembodiments may operate without a memory. The video processing hardware2906 may be used to implement, in hardware circuitry, some techniquesdescribed in the present document.

In some embodiments, the video coding methods may be implemented usingan apparatus that is implemented on a hardware platform as describedwith respect to FIG. 29.

Some embodiments of the disclosed technology include making a decisionor determination to enable a video processing tool or mode. In anexample, when the video processing tool or mode is enabled, the encoderwill use or implement the tool or mode in the processing of a block ofvideo, but may not necessarily modify the resulting bitstream based onthe usage of the tool or mode. That is, a conversion from the block ofvideo to the bitstream representation of the video will use the videoprocessing tool or mode when it is enabled based on the decision ordetermination. In another example, when the video processing tool ormode is enabled, the decoder will process the bitstream with theknowledge that the bitstream has been modified based on the videoprocessing tool or mode. That is, a conversion from the bitstreamrepresentation of the video to the block of video will be performedusing the video processing tool or mode that was enabled based on thedecision or determination.

Some embodiments of the disclosed technology include making a decisionor determination to disable a video processing tool or mode. In anexample, when the video processing tool or mode is disabled, the encoderwill not use the tool or mode in the conversion of the block of video tothe bitstream representation of the video. In another example, when thevideo processing tool or mode is disabled, the decoder will process thebitstream with the knowledge that the bitstream has not been modifiedusing the video processing tool or mode that was enabled based on thedecision or determination.

FIG. 30 is a block diagram showing an example video processing system3000 in which various techniques disclosed herein may be implemented.Various implementations may include some or all of the components of thesystem 3000. The system 3000 may include input 3002 for receiving videocontent. The video content may be received in a raw or uncompressedformat, e.g., 8 or 10 bit multi-component pixel values, or may be in acompressed or encoded format. The input 3002 may represent a networkinterface, a peripheral bus interface, or a storage interface. Examplesof network interface include wired interfaces such as Ethernet, passiveoptical network (PON), etc. and wireless interfaces such as Wi-Fi orcellular interfaces.

The system 3000 may include a coding component 3004 that may implementthe various coding or encoding methods described in the presentdocument. The coding component 3004 may reduce the average bitrate ofvideo from the input 3002 to the output of the coding component 3004 toproduce a coded representation of the video. The coding techniques aretherefore sometimes called video compression or video transcodingtechniques. The output of the coding component 3004 may be eitherstored, or transmitted via a communication connected, as represented bythe component 3006. The stored or communicated bitstream (or coded)representation of the video received at the input 3002 may be used bythe component 3008 for generating pixel values or displayable video thatis sent to a display interface 3010. The process of generatinguser-viewable video from the bitstream representation is sometimescalled video decompression. Furthermore, while certain video processingoperations are referred to as “coding” operations or tools, it will beappreciated that the coding tools or operations are used at an encoderand corresponding decoding tools or operations that reverse the resultsof the coding will be performed by a decoder.

Examples of a peripheral bus interface or a display interface mayinclude universal serial bus (USB) or high definition multimediainterface (HDMI) or Displayport, and so on. Examples of storageinterfaces include SATA (serial advanced technology attachment), PCI,IDE interface, and the like. The techniques described in the presentdocument may be embodied in various electronic devices such as mobilephones, laptops, smartphones or other devices that are capable ofperforming digital data processing and/or video display.

From the foregoing, it will be appreciated that specific embodiments ofthe presently disclosed technology have been described herein forpurposes of illustration, but that various modifications may be madewithout deviating from the scope of the invention. Accordingly, thepresently disclosed technology is not limited except as by the appendedclaims.

Implementations of the subject matter and the functional operationsdescribed in this patent document can be implemented in various systems,digital electronic circuitry, or in computer software, firmware, orhardware, including the structures disclosed in this specification andtheir structural equivalents, or in combinations of one or more of them.Implementations of the subject matter described in this specificationcan be implemented as one or more computer program products, i.e., oneor more modules of computer program instructions encoded on a tangibleand non-transitory computer readable medium for execution by, or tocontrol the operation of, data processing apparatus. The computerreadable medium can be a machine-readable storage device, amachine-readable storage substrate, a memory device, a composition ofmatter effecting a machine-readable propagated signal, or a combinationof one or more of them. The term “data processing unit” or “dataprocessing apparatus” encompasses all apparatus, devices, and machinesfor processing data, including by way of example a programmableprocessor, a computer, or multiple processors or computers. Theapparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a stand-alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Computer readable media suitable for storingcomputer program instructions and data include all forms of nonvolatilememory, media and memory devices, including by way of examplesemiconductor memory devices, e.g., EPROM, EEPROM, and flash memorydevices. The processor and the memory can be supplemented by, orincorporated in, special purpose logic circuitry.

It is intended that the specification, together with the drawings, beconsidered exemplary only, where exemplary means an example. As usedherein, the use of “or” is intended to include “and/or”, unless thecontext clearly indicates otherwise.

While this patent document contains many specifics, these should not beconstrued as limitations on the scope of any invention or of what may beclaimed, but rather as descriptions of features that may be specific toparticular embodiments of particular inventions. Certain features thatare described in this patent document in the context of separateembodiments can also be implemented in combination in a singleembodiment. Conversely, various features that are described in thecontext of a single embodiment can also be implemented in multipleembodiments separately or in any suitable subcombination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Moreover, the separation of various system components in theembodiments described in this patent document should not be understoodas requiring such separation in all embodiments.

Only a few implementations and examples are described and otherimplementations, enhancements and variations can be made based on whatis described and illustrated in this patent document.

What is claimed is:
 1. A method of processing video data, comprising: determining, based on one or more picture order count (POC) parameters associated with a picture of a current video block of a video, whether a first prediction mode can be applied, wherein the first prediction mode is a bi-directional optical flow (BDOF) prediction mode, and in the first prediction mode, prediction samples of the current video block is refined at least based on gradients in different directions; and performing, based on the determining, a conversion between the current video block and a bitstream of the video; wherein a first reference picture (R₀) and a second reference picture (R₁) are associated with a current picture comprising the current video block, wherein τ₀ is a difference between a picture order count of the current picture and a picture order count of the first reference picture (T₀=POC(current)−POC(R₀)), and wherein τ₁ is a difference between a picture order count of the second reference picture and the picture order count of the current picture (τ₁=POC(R₁)−POC(current)).
 2. The method of claim 1, further comprising: refraining from enabling the first prediction mode in response to abs(abs(τ₀)−abs(τ₁))≥T₀, wherein T₀ is an integer, and T₀ is larger than
 0. 3. The method of claim 1, further comprising: refraining from enabling the first prediction mode in response to abs(τ₀)≥abs(τ₁)×T₀ or abs(τ₁)≥abs(τ₀)×T₀, wherein T₀ is an integer.
 4. The method of claim 3, wherein T₀=1.
 5. The method of claim 2, wherein T₀ is signaled in a video parameter set (VPS), a sequence parameter set (SPS), a picture parameter set (PPS), a slice header, a tile group header, a tile, a coding unit (CU) or a coding tree unit (CTU).
 6. The method of claim 1, wherein the conversion comprises decoding the current video block from the bitstream.
 7. The method of claim 1, wherein the conversion comprises encoding the current video block into the bitstream.
 8. An apparatus for processing video data comprising a processor and a non-transitory memory with instructions thereon, wherein the instructions upon execution by the processor, cause the processor to: make a determination, based on one or more picture order count (POC) parameters associated with a picture of a current video block of a video, whether a first prediction mode can be applied, wherein the first prediction mode is a bi-directional optical flow (BDOF) prediction mode, and in the first prediction mode, prediction samples of the current video block is refined at least based on gradients in different directions; and perform, based on the determination, a conversion between the current video block and a bitstream of the video; wherein a first reference picture (R₀) and a second reference picture (R₁) are associated with a current picture comprising the current video block, wherein τ₀ is a difference between a picture order count of the current picture and a picture order count of the first reference picture (τ₀=POC(current)−POC(R₀)), and wherein τ₁ is a difference between a picture order count of the second reference picture and the picture order count of the current picture (τ₁=POC(R₁)−POC(current)).
 9. The apparatus of claim 8, wherein the processor is further caused to: refrain from enabling the first prediction mode in response to abs(abs(τ₀)−abs(τ₁))≥T₀, wherein T₀ is an integer, and T₀ is larger than
 0. 10. The apparatus of claim 8, wherein the processor is further caused to: refrain from enabling the first prediction mode in response to abs(τ₀)≥abs(τ₁)×T₀ or abs(τ₁)≥abs(τ₀)×T₀, wherein T₀ is an integer.
 11. The apparatus of claim 10, wherein T₀=1.
 12. The apparatus of claim 9, wherein T₀ is signaled in a video parameter set (VPS), a sequence parameter set (SPS), a picture parameter set (PPS), a slice header, a tile group header, a tile, a coding unit (CU) or a coding tree unit (CTU).
 13. The apparatus of claim 8, wherein the conversion comprises decoding the current video block from the bitstream.
 14. The apparatus of claim 8, wherein the conversion comprises encoding the current video block into the bitstream.
 15. A non-transitory computer-readable storage medium storing instructions that cause a processor to: make a determination, based on one or more picture order count (POC) parameters associated with a picture of a current video block of a video, whether a first prediction mode can be applied, wherein the first prediction mode is a bi-directional optical flow (BDOF) prediction mode, and in the first prediction mode, prediction samples of the current video block is refined at least based on gradients in different directions; and perform, based on the determination, a conversion between the current video block and a bitstream of the video; wherein a first reference picture (R₀) and a second reference picture (R₁) are associated with a current picture comprising the current video block, wherein τ₀ is a difference between a picture order count of the current picture and a picture order count of the first reference picture (T₀=POC(current)−POC(R₀)), and wherein τ₁ is a difference between a picture order count of the second reference picture and the picture order count of the current picture (τ₁=POC(R₁)−POC(current)).
 16. The non-transitory computer-readable storage medium of claim 15, wherein the processor is further caused to: refrain from enabling the first prediction mode in response to abs(abs(τ₀)−abs(τ₁))≥T₀, wherein T₀ is an integer, and T₀ is larger than
 0. 17. The non-transitory computer-readable storage medium of claim 15, wherein the processor is further caused to: refrain from enabling the first prediction mode in response to abs(τ₀)≥abs(τ₁)×T₀ or abs(τ₁)≥abs(τ₀)×T₀, wherein T₀ is an integer.
 18. The non-transitory computer-readable storage medium of claim 17, wherein T₀=1.
 19. The non-transitory computer-readable storage medium of claim 16, wherein T₀ is signaled in a video parameter set (VPS), a sequence parameter set (SPS), a picture parameter set (PPS), a slice header, a tile group header, a tile, a coding unit (CU) or a coding tree unit (CTU).
 20. A non-transitory computer-readable recording medium storing a bitstream of a video which is generated by a method performed by a video processing apparatus, the method comprising: make a determination, based on one or more picture order count (POC) parameters associated with a picture of a current video block of a video, whether a first prediction mode can be applied, wherein the first prediction mode is a bi-directional optical flow (BDOF) prediction mode, and in the first prediction mode, prediction samples of the current video block is refined at least based on gradients in different directions; and generate, based on the determination, the bitstream; wherein a first reference picture (R₀) and a second reference picture (R₁) are associated with a current picture comprising the current video block, wherein τ₀ is a difference between a picture order count of the current picture and a picture order count of the first reference picture (T₀=POC(current)−POC(R₀)), and wherein τ₁ is a difference between a picture order count of the second reference picture and the picture order count of the current picture (τ₁=POC(R₁)−POC(current)). 